MétaCan
Menu
Back to cohort
Record W2329855816 · doi:10.1155/2002/919212

A Troubling Story: Insurance and Medical Research in Saskatchewan

2002· editorial· en· W2329855816 on OpenAlexaboutno aff
Harold Merskey, Robert Teasell

Bibliographic record

VenuePain Research and Management · 2002
Typeeditorial
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsFunction (biology)Alternative medicinePain managementSocial insurancePublic relationsPublic health insuranceMedical researchPain medicinePsychologyHealth careSociologyMedicineHealth insuranceLawPsychiatryPolitical science

Abstract

fetched live from OpenAlex

It is standard teaching that medicine and health care function in a social setting, and the pain literature is full of material on social and psychological factors. In Pain Research & Management, we drew attention to the impact of social influences on medical thinking and the readiness with which sections of the medical profession sided with paymasters for insurance companies (1). At that time, we were well aware of the most troubling story in decades in the fields of pain, insurance and medical research (2,3). In her commentary in this issue of Pain Research & Management, Lorie Terry (pages 101 to 106) provides information about why the scientific commentators have been radically critical of an article by Cassidy et al, published in The New England Journal of Medicine (2), on the basis of both internal evidence and additional information that accrued, some of which was available at the time of the article′s publication and some of which subsequently became public.

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)0.019
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0100.007
Scholarly communication0.0110.005
Open science0.0040.002
Research integrity0.0330.046
Insufficient payload (model declined to judge)0.0040.002

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 head0.187
GPT teacher head0.554
Teacher spread0.367 · 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
DomainIncentives
GenreEditorial

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

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same venuePain Research and ManagementSame topicPublic Health Policies and EducationFrench-language works237,207