Bibliographic record
Abstract
Drawing policy conclusions from uncontrolled studies Studying a population of adults seenin clinic for biliary colic, Boris Sobolev and associates1 documented an association between longer waiting times and admissions for emergency cholecystectomy. However, because the patients were not randomly assigned to the waiting list, readers should entertain the possi-bility that the findings were driven by an association with a so-called “third variable. ” For example, the patients who ended up on the waiting list might have been sicker. If so, the observed as-sociation between waiting times and emergency admissions was actually dri-ven by an unobserved association be-tween health status and emergency ad-missions. Sobolev and associates1 acknowledge the possibility of con-founding by patient morbidity, and they do attempt rudimentary adjust-ment for other potential confounding variables. However, even if they had had access to better data on patients’ health status, the criticism of potential confounding would remain. Prior studies, none of which were cited by Sobolev and associates,1 have addressed this problem by means of econometric methodology.2–4 Hamilton and colleagues2 used an estimation strategy that accounted for unmeasured health differences and found no effect of waiting times on death rates for pa-tients waiting for hip fracture surgery. Subsequent comparisons of patients with hip fracture in the United States and Canada3,4 arrived at a similar con-clusion. Policy-makers seeking to draw con-clusions from the findings of Sobolev and associates1 would be well advised to consider these more sophisticated econometric analyses in their delibera-tions.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.378 | 0.222 |
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".