Generating a list of potentially important contextual factors covering randomized trials, cohorts, and measurement property studies: An OMERACT initiative
Bibliographic record
Abstract
OBJECTIVES: To generate candidates for contextual factors (CFs) for each CF type (i.e., Effect Modifying Contextual Factors (EM-CFs), Outcome Influencing Contextual Factors (OI-CFs), and Measurement Affecting Contextual Factors (MA-CFs)) considered important within rheumatology. METHODS: We surveyed OMERACT working groups and conducted a Special Interest Group (SIG) session at the OMERACT 2023 meeting, where the results were reviewed, and additional CFs suggested. RESULTS: The working groups suggested 44, 49, and 21 generic EM-CFs, OI-CFs, and MA-CFs, respectively. SIG participants added 49, 44, and 55 factors, respectively. CONCLUSION: Candidate CFs were identified, next step is a consensus-based set of endorsed (important) CFs.
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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 | 0.679 | 0.756 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".