An OMERACT Initiative Toward Consensus to Identify and Characterize Candidate Contextual Factors: Report from the Contextual Factors Working Group
Bibliographic record
Abstract
OBJECTIVE: The importance of contextual factors (CF) for appropriate patient-specific care is widely acknowledged. However, evidence in clinical trials on how CF influence outcomes remains sparse. The 2014 Outcome Measures in Rheumatology (OMERACT) Handbook introduced the role of CF in outcome assessment and defined them as "potential confounders and/or effect modifiers of outcomes in randomized controlled trials." Subsequently, the CF Methods Group (CFMG) was formed to develop guidance on how to address CF in clinical trials. METHODS: First, the CFMG conducted an e-mail survey of OMERACT working groups (WG) to analyze how they had addressed CF in outcome measurement so far. The results facilitated an informed discussion at the OMERACT 2016 CFMG Special Interest Group (SIG) session, with the aim of gaining preliminary consensus regarding an operational definition of CF and to make a first selection of potentially relevant CF. RESULTS: The survey revealed that the WG had mostly used the OMERACT Handbook and/or the International Classification of Functioning, Disability and Health (ICF) definition. However, significant heterogeneity was found in the methods used to identify, refine, and categorize CF candidates. The SIG participants agreed on using the ICF as a framework along with the OMERACT Handbook definition. A list with 28 variables was collected including person-related factors and physical and social environments. Recommendations from the SIG guided the CFMG to formulate 3 preliminary projects on how to identify and analyze CF. CONCLUSION: New methods are urgently needed to assist researchers to identify and characterize CF that significantly influence the interpretation of results in clinical trials. The CFMG defined first steps to develop further guidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.337 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".