Race, Ethnicity, and Sepsis: Beyond Adjusted Odds Ratios*
Bibliographic record
Abstract
Department of Critical Care Medicine, Guy’s and St Thomas’ NHS Foundation Trust, School of Immunology & Microbial Sciences, Kings College London, London, United Kingdom Interdepartmental Division of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada *See also p. 878. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the U.K. Department of Health. Dr. Shankar-Hari wrote the first draft of the article. Dr. Shankar-Hari/Rubenfeld contributed to the critical revision of the article and approved the final article. Dr. Shankar-Hari is supported by the U.K. National Institute for Health Research Clinician Scientist Award (NIHR-CS-2016-16-011). Dr. Rubenfeld has disclosed that he does not have any potential conflicts of interest.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".