Generating a list of potentially important contextual factors covering Randomized trials, Cohorts, and Measurement property studies: An OMERACT initiative
Bibliographic record
Abstract
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. 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. 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. 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 | 0.650 | 0.743 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.029 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".