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Record W1577225265

The Medical Malpractice Explosion: An Empirical Assessment of Trends, Determinants, and Impacts

2008· article· en· W1577225265 on OpenAlexaffabout
David G. Duff, Michael J. Trebilcock, Donald N. Dewees

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsLiabilityMalpracticeMedical malpracticeTortEmpirical evidenceActuarial scienceEmpirical researchPersonal injuryEconomicsPolitical scienceLawAccounting
DOInot available

Abstract

fetched live from OpenAlex

This article briefly describes trends in the frequency and severity of medical malpractice claims in Canada and the U.S., with some comparative references to trends in Britain and Australia. In all cases, frequency and severity rates appear to have risen quite dramatically over the past decade and a half. The article proceeds to explore various hypotheses that might explain these trends. While empirical analysis does not yield firm conclusions, the fact that so many jurisdictions have experienced a somewhat similar phenomenon makes it doubtful that the primary cause of the increase is likely to be idiosyncratic features of one particular country's tort system. Instead, the authors conjecture that various changes in medical technology may well be a more important explanatory factor. The article goes on to examine the empirical evidence on the impact of expanding liability on physician behaviour and in turn whether observed changes in physician behaviour have caused reductions in the medical injury rate. While it seems clear from the evidence that the liability system has 'induced various changes in physician behaviour, it is much less clear whether these changes have reduced the medical injury rate or are otherwise socially desirable.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.502
Teacher spread0.426 · 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 designObservational
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

Citations1
Published2008
Admission routes2
Has abstractyes

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