Population-level trends in emergency general surgery presentations and mortality over time
Bibliographic record
Abstract
Journal Article Population-level trends in emergency general surgery presentations and mortality over time Get access Jordan Nantais, Jordan Nantais Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, CanadaSection of General Surgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, Canada Correspondence to: Jordan Nantais, Section of General Surgery, Department of Surgery, University of Manitoba, GF436B, 820 Sherbrook Street, Winnipeg, Manitoba, Canada, R3K 0V6 (e-mail: jnantais@hsc.mb.ca) https://orcid.org/0000-0003-3358-1983 Search for other works by this author on: Oxford Academic Google Scholar Nancy N Baxter, Nancy N Baxter Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, CanadaICES, Toronto, Ontario, CanadaDepartment of Surgery, University of Toronto, Toronto, Ontario, CanadaManagement and Evaluation, Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto, Toronto, Ontario, CanadaTemerty Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, Ontario, CanadaMelbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia Search for other works by this author on: Oxford Academic Google Scholar Refik Saskin, Refik Saskin ICES, Toronto, Ontario, Canada Search for other works by this author on: Oxford Academic Google Scholar Sarvesh Logsetty, Sarvesh Logsetty Section of General Surgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, CanadaSection of Plastic Surgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, CanadaDepartment of Pediatrics and Child Health, University of Manitoba, Winnipeg, Manitoba, CanadaDepartment of Psychiatry, University of Manitoba, Winnipeg, Manitoba, Canada Search for other works by this author on: Oxford Academic Google Scholar David Gomez David Gomez Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, CanadaICES, Toronto, Ontario, CanadaDepartment of Surgery, University of Toronto, Toronto, Ontario, CanadaManagement and Evaluation, Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto, Toronto, Ontario, CanadaTemerty Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, Ontario, CanadaDivision of General Surgery, St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada Search for other works by this author on: Oxford Academic Google Scholar British Journal of Surgery, znad041, https://doi.org/10.1093/bjs/znad041 Published: 04 March 2023 Article history Received: 14 October 2022 Revision received: 05 January 2023 Accepted: 01 February 2023 Published: 04 March 2023
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".