Causality, Learning and Forgetting in Surgery
Bibliographic record
Abstract
In this paper we distinguish between two causal explanations for the volume-outcome relationship, learning-by-doing and selective referral. We use data on three surgical procedures for which a volume-outcome relationship has been documented, the Whipple, coronary artery bypass graft (CABG) and repair of abdominal aortic aneurysm (AAA). We distinguish between the competing explanations by estimating the relationship between mortality and volume in a non-linear system of equations where volume is endogenous and where predicted volume is used to “instrument ” for volume. In this system, we also allow for the possibility of forgetting. This model will identify learning-by-doing by the extent to which differences in expected volume based on variation in the numbers of competitors and patients near the hospital affect mortality at the hospital. For AAA and CABG increased volume appears to cause lower mortality, while the direction of causality is less certain for the Whipple. Using the assumption that volume is exogenous, we find that a significant amount of the learning is retained from quarter to quarter for the Whipple and AAA. For CABG, the impact of an exogenous increase in contemporaneous volume on mortality depreciates from one quarter to the next.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".