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Record W2625492709 · doi:10.60082/2817-5069.3158

Canada’s First Malpractice Crisis: Medical Negligence in the Late Nineteenth Century

2017· article· en· W2625492709 on OpenAlexaffvenueabout
Richard B. Brown

Bibliographic record

VenueOsgoode Hall law journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsMalpracticePlaintiffMedical malpracticeLawAttendancePolitical scienceMedicineHistory

Abstract

fetched live from OpenAlex

This article describes and explains the first Canadian medical malpractice crisis. While malpractice had emerged as a prominent legal issue in the United States by the mid nineteenth century, Canadian doctors first began to express concerns with a growth in malpractice litigation in the late nineteenth century. Physicians claimed that lawsuits damaged reputations and forced them to spend lavishly on defending themselves. Doctors blamed lawyers for drumming up spurious lawsuits and argued that ignorant or malicious jurors tended to side with plaintiffs. Evidence, however, points to additional factors that contributed to litigation. Medical professionals in rural areas sometimes avoided lengthy travel, leading to allegations of malpractice when patient health declined despite calls for attendance. As the number of doctors increased in Canada, some physicians may have encouraged negligence suits against their competitors. Late nineteenth-century claims to professionalism also played a role. Patients came to expect better outcomes, especially in orthopedics, which dominated most of the reported instances of malpractice in the period.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
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 score0.954
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.055
GPT teacher head0.408
Teacher spread0.352 · 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 teacher head, 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

Citations7
Published2017
Admission routes3
Has abstractyes

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