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Record W3009745939 · doi:10.1503/cjs.003319

Necessity is the mother of invention: William Stewart Halsted’s addiction and its influence on the development of residency training in North America

2020· article· en· W3009745939 on OpenAlexaffvenue
James R. Wright, Norman S. Schachar

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineResidency trainingAddictionMedical educationTraining (meteorology)GerontologyPsychiatryContinuing education

Abstract

fetched live from OpenAlex

Summary: William Stewart Halsted developed a novel residency training program at Johns Hopkins Hospital that, with some modifications, became the model for surgical and medical residency training in North America. While performing anesthesia research early in his career, Halsted became addicted to cocaine and morphine. This paper dissects how his innovative multi-tier residency program helped him hide his addiction while simultaneously providing outstanding patient care and academic training.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.013
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.135
GPT teacher head0.225
Teacher spread0.090 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2020
Admission routes2
Has abstractyes

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