Health Evidence Knowledge Accelerator (HEvKA) Daily Progress December 19, 2025

[Brian Alper]

Friday December 19, 2025

 

10 people (AS, BA, HK, HL, JT, KS, KW, MA, PW, SM) participated in up to 3 active working group meetings. 

 

The Statistics Terminology Working Group reviewed the hierarchy of categorical variable terms and noted several incongruencies. Polychotomous was a parent of dichotomous in one area, but a sibling in another. We decided to re-defined polychotomous as 3 or more categories and make it mutually exclusive from dichotomous. Also, multiclass categorical variable was classified as a type of nominal variable, but we moved it in the hierarchy as multiclass ordinal variables could occur as well. We reopened terms for vote as needed to match these changes to the hierarchy.  The 7 terms open for vote now are:

 

 

Term	Definition	Alternative Terms (if any)	Comment for application (if any)
categorical variable	A variable that can assume values from only a finite set of values.		Categorical variables are variables that can assume non-quantitative values, regardless of whether or not the categories (i.e. possible values) are identified with numbers.
dichotomous variable	A nominal variable that can assume values from 2 discrete possible values.	dichotomous nominal variable binary variable binary nominal variable	A dichotomous variable is a categorical variable in which the number of categories (i.e. possible values) is exactly 2. A categorical variable may be classified as either a nominal variable or an ordinal variable. In SEVCO, 'dichotomous variable' is used synonymously with 'dichotomous nominal variable' because the common term when used without specification of nominal vs. ordinal is presumed to be nominal without an ordering of the categories.
boolean variable	A nominal variable that can assume values from 2 discrete possible values, True of False.	Boolean variable
polychotomous variable	A nominal variable that can assume values from 3 or more discrete possible values.	polychotomous nominal variable	A categorical variable may be classified as either a nominal variable or an ordinal variable. In SEVCO, 'polychotomous variable' is used synonymously with 'polychotomous nominal variable' because the common term when used without specification of nominal vs. ordinal is presumed to be nominal without an ordering of the categories.
dichotomous ordinal variable	An ordinal variable that can assume values from 2 discrete possible values.	binary ordinal variable	A dichotomous ordinal variable is a categorical variable in which the number of categories (i.e. possible values) is exactly 2. A categorical variable may be classified as either a nominal variable or an ordinal variable. In SEVCO, 'dichotomous variable' is used synonymously with 'dichotomous nominal variable' because the common term when used without specification of nominal vs. ordinal is presumed to be nominal without an ordering of the categories.
polychotomous ordinal variable	An ordinal variable that can assume values from 3 or more discrete possible values.		Examples include a rating scale for pain (e.g., "low," "medium," "high"), a customer satisfaction score (e.g., "poor," "fair," "good," "excellent"), degree of education achieved, or a Likert scale.
multiclass categorical variable	A categorical variable that can assume two or more values simultaneously.	multilabel categorical variable	An example of a multiclass categorical variable is race, as an individual can identify with two or more races.

 

To make voting for multiple terms easier, please use the MyBallot software on the FEvIR Platform https://fevir.net/myballot.

 

To participate you can join the Scientific Evidence Code System (SEVCO) Expert Working Group at https://fevir.net/resources/Composition/27845.

 

The GRADE Ontology Working Group discussed and revised 3 terms.  There will not be a meeting of this group on December 26 so January 2 will be the next meeting.

 

For Outcome Importance, we discussed feedback from the GRADE Guidance Group regarding concerns that outcome importance, outcome values, and utility values may or may not be interchangeable so we removed the Alternative Terms.

 

For Limitations in study design and execution, we discussed negative votes with concerns about using the term “error” and the need to comment on inadequate reporting of study methods, so revised the Definition and Comment for application to remove these concepts.

 

For Inconsistency, we changed ‘findings’ to ‘study results’ in the Comment for application to match changes that we made in the Definition.

 

6 terms open for voting are:

 

Term	Definition	Alternative Terms (if any)	Comment for application (if any)
Limitations in study design and execution	Issues in the design, conduct, or analysis of one or more studies that may introduce systematic error into a body of evidence.	Risk of bias Study limitations Internal validity concerns Risk of bias concerns	Error is a difference between the reported results of one or more studies (findings, conclusions, or effect estimates) and the actuality (the truth, or the true value targeted for estimation). Error may be random error or systematic error. Bias is a systematic error, or systematic deviation from the truth. In GRADE, the term "Limitations in study design and execution" exclusively concerns bias due to issues with the internal validity of a body of evidence (one or more studies). Insufficient reporting of study methods (e.g., lack of reporting allocation concealment) may be considered a type of Limitations in study design and execution. Other sources of bias (indirectness, dissemination bias, and inconsistency) are dealt with separately in GRADE. Historically, the GRADE approach used 'study quality' and, to align with Cochrane terminology, later 'risk of bias' as the preferred term. 'Limitations in study design and execution' is more semantically accurate, but some use risk of bias synonymously. In addition, the term 'publication bias' was changed to 'dissemination bias'. With these changes, the concept of selective outcome reporting may be classified as Limitations in study design and execution or may be classified as Dissemination bias, depending on the tools and systems used. Users of GRADE should be cautious to avoid rating down twice for the same concerns.
Inconsistency	Variation in the study results that are being synthesized.		Inconsistency is one of the domains that can impact the rating of the certainty of evidence for a synthesis result. The synthesis result can be an estimate of effect, a measure other than effect (e.g., incidence, prevalence, sensitivity, specificity), or a narrative summary. Any variation in findings could be classified as inconsistency. The degree of inconsistency may range from being almost non-existent to large. The degree of inconsistency may be expressed as a measure of statistical heterogeneity. Inconsistency between direct and indirect evidence in a network meta-analysis is referred to as "incoherence" in GRADE.
Dissemination bias	Systematic differences between the included evidence and the evidence that would have met the eligibility criteria but was not available or accessible at the time of review.	Publication bias	Dissemination bias may result from issues with the availability or accessibility of study results. Availability is whether the study results exist and can be obtained. Accessibility is how easily the results can be obtained. Factors that influence if, how, when, and where study results are published (made publicly available) may include the direction and magnitude of effect, statistical significance, resources of the study authors, location of the investigation, or conflicts of interest. Publicly available study results may not be accessible to the review authors at the time of analysis due to financial barriers (e.g. whether researchers can afford access to articles); language barriers; or indexing barriers, such as publication as abstracts, theses, conference proceedings, preprints, or other publication formats that may be less likely to be indexed or otherwise more difficult to retrieve. Dissemination bias can distort the effect estimate, either overestimating or underestimating the effect. The extent and direction of this distortion depend on whether the study hypothesis predicts a positive or negative effect and whether dissemination incentives favor making certain results more accessible than others. There are several types of dissemination bias. These include, but are not limited to, publication bias, in which the published studies are not representative of all conducted studies, and selective outcome reporting bias, in which the results presented in a study are not representative of all the results generated by the study. This domain was previously named 'publication bias'. The updated terminology in the GRADE Book changed this domain to 'dissemination bias', reflecting a broader understanding of biases affecting the dissemination of research findings. The concept of selective outcome reporting may be classified as Limitations in study design and execution or may be classified as Dissemination bias, depending on the tools and systems used. Users of GRADE should be cautious to avoid rating down twice for the same concerns.
Undesirable Effects on Health and Wellbeing	The expected health- and wellbeing-specific consequences of the intervention that are considered to be a worsening or decline.	Harms Harms and Burdens Undesirable Consequences Harmful Effects Undesirable Effects Undesirable Health Effects Undesirable Health and Wellbeing Effects	Undesirable effects are the expected differences in outcomes that favor the comparator in a comparison between the intervention and comparator. Examples of undesirable effects may include short-term or long-term harms (such as death, pain, nausea, disability, or lower quality of life) and burdens (such as interference with social functioning or interference with day-to-day life). Undesirable effects may also be called negative outcomes but 'harmful' or 'undesirable' is preferred instead of 'negative' to avoid confusion between the direction of effect and the desirability of the outcome.
Outcome Importance	The relative importance of the outcomes that are being considered for the balance of effects from the perspective of the people affected by the decision.		In the evidence-to-decision framework, the Outcome Importance domain relates to the uncertainty about or variability in how much people value the outcomes that are being considered for the balance of effects. "Value" in this context refers to the relative importance of the outcomes of interest. Early versions of the GRADE approach addressed outcome importance under a broader concept of "values and preferences" that encompassed the perspectives, beliefs, expectations, and goals for health and life that lead an individual to preferring one intervention over another, and also included an individual's consideration of potential benefits, harms, costs, and inconvenience of an intervention when deciding their preference. GRADE has since separated the various concepts encompassed by values and preferences into separate domains for Equity, Acceptability, and Feasibility alongside Outcome Importance.
Impact on Equity	The effect of the decision on unfair or unjust differences in health or other outcomes between groups.	Impact on Inequity Equity considerations Inequity considerations Impact on Health Equity Fairness considerations Impact on Fairness	A health outcome is a measurable change in health status that results from an intervention, exposure, or other health determinant. Other outcomes may include changes in financial status, social status, or any other status to which an equity judgment may apply. Judgments regarding equity typically involve identifying and assessing the differential impact of decisions on groups that may be socially disadvantaged due to factors such as place of residence, race, ethnicity, occupation, gender, sex, religion, education, socioeconomic status, or social capital. The objective is to identify whether there are inequalities in the effects generated by the intervention and consider whether these constitute an equity issue that needs to be addressed. Whether equity is limited to health equity or considers other outcomes depends on the perspectives taken in the evidence-to-decision framework.

 

To make voting on multiple terms easier, please use the MyBallot Software on the FEvIR Platform. https://fevir.net/myballot

 

The Project Management Working Group prepared the agenda for upcoming meetings:

 

You can join any of the groups that are now meeting in the following Weekly Meeting Schedule: Please note all HEvKA meetings are in Eastern Time (ET) in the United States.

Day/Time (Eastern US)	Working Group	Agenda Items
Monday 8-9 am	Project Management	FHIR changes, IG QA
Monday 11 am-12 pm	StudyRegistryRecord Profile (a BRR EBMonFHIR sub-WG)	Draft ResearchStudySite Profile, Advance CTIS mapping with CTIS engineers
Tuesday 9-10 am	Measuring the Rate of Scientific Knowledge Transfer (SKAF Board meeting monthly)	Dec 23: CANCELLED, Dec 30: SKAF Board, Jan 6: Finish reviewing initial study results for index articles, Introduction for new investigators
Tuesday 2-3 pm	StatisticsOnFHIR (a CDS EBMonFHIR sub-WG)	Review SEVCO 2.0/3.0 introductory manuscript for submission (will move to Mondays 2-3 pm in January)
Wednesday 8-9 am	Computable EBM Tools Development	CANCELLED FOR HOLIDAYS, next meeting January 7
Wednesday 9-10 am	Communications (Awareness, Scholarly Publications)	CANCELLED FOR HOLIDAYS, next meeting January 7
Thursday 8-9 am	EBMonFHIR Implementation Guide (a CDS EBMonFHIR sub-WG) (GINTech every 3 months)	CANCELLED FOR HOLIDAYS, next meeting January 8: Coordinate with AWMF Dissolve-E derivative IG for Guideline Registry
Thursday 9-10 am	Making Guidelines Computable	CANCELLED FOR HOLIDAYS, next meeting January 8: Start new project (e.g. working with heart failure guideline )
Friday 9-10 am	Statistic Terminology	CANCELLED DEC 26; Jan 2: Discuss terms open for voting (types of categorical variable)
Friday 10-11 am	GRADE Ontology	CANCELLED DEC 26; Jan 2: Discuss terms open for voting (Impact on equity. Inconsistency, Limitations in study design and execution)
Friday 12-1 pm	Project Management	CANCELLED DEC 26; Jan 2: Prepare next week's meeting agenda

                                     

 

Releases today on the FEvIR Platform:

 

FEvIR® Platform Release 1.16.0 (December 19, 2025) defaults to mobile view when accessing the FEvIR Platform from a mobile device.

 

 

Quote for Thought:

 

“It’s beginning to cost a lot like Christmas.”  –Krampus(2015)

 

 

 

 

To get involved or stay informed: HEvKA Project Page on FEvIR Platform, HEvKA Project Page on HL7 Confluence, View a consolidated list of presentations, videos, posters, journal articles, and book chapter mentions related to HEvKA, COKA, EBMonFHIR and the FEvIR Platform 

at https://ComputablePublishing.com/media. or join any of the groups that are now meeting in the following weekly schedule:

(The current week’s agenda can always be found at: https://fevir.net/resources/Composition/29272).  

Weekly Meeting Schedule and Link: 

Day	Time (Eastern)	Team
Monday	8-9 am	Project Management WG
Monday	11 am - 12 pm	StudyRegistryRecordProfile WG (a BRR EBMonFHIR sub-WG)
Tuesday	9-10 am	Measuring the Rate of Scientific Knowledge Transfer WG
Tuesday	2-3 pm	StatisticsOnFHIR WG (a CDS EBMonFHIR sub-WG)
Wednesday	8-9 am	Computable EBM Tools Development WG
Wednesday	9-10 am	Communications(Awareness, Scholarly Publications) WG
Thursday	8-9 am	EBM Implementation Guide WG (a CDS EBMonFHIR sub-WG)
Thursday	9-10 am	Making Guidelines Computable WG
Friday	9-10 am	Statistic Terminology WG *New Time
Friday	10-11 am	GRADE Ontology WG
Friday	12-1 pm	Project Management WG

 

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Brian S. Alper, MD, MSPH, FAAFP, FAMIA, FGIN

CEO, Computable Publishing LLC

bal...@computablepublishing.com

 

 

Making Science Machine-Interpretable
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Lead, HEALTH EVIDENCE KNOWLEDGE ACCELERATOR

President, Scientific Knowledge Accelerator Foundation

 

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