MétaCan
Menu
Back to cohort
Record W2332306138 · doi:10.1155/2000/657856

The Montreal General Hospital Pain Centre (1974‐2000): The Contributions of Ronald Melzack

2000· article· en· W2332306138 on OpenAlexaffabout
Mary Ellen Jeans, Joseph Stratford, Paul Taenzer, Sandra LeFort, Kathleen Rowat

Bibliographic record

VenuePain Research and Management · 2000
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMemorial University of NewfoundlandMontreal General HospitalCanadian Nurses Association
Fundersnot available
KeywordsDreamMultidisciplinary approachPsychologyGeneral hospitalMEDLINEMedicineSociologyFamily medicinePsychotherapistPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

This paper chronicles the development of the Montreal General Hospital Pain Centre from its inception in 1974 to the present. Highlighted in particular are the contributions of Ronald Melzack to this history. Data for the article arose, in the main, from an interview with Dr Melzack carried out earlier in the year. Discussions with former and present members of the pain centre team, including former graduate students, provided additional information. The article begins with a recounting of those individuals and events that inspired Ron early in his ′pain career′ to pursue his dream of a multidisciplinary pain centre, the first of its kind in Canada. The forces that helped shape the development of this centre and the challenges that had to be overcome are described.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.443
Threshold uncertainty score0.880

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.007
Scholarly communication0.0070.003
Open science0.0020.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0110.002

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 head0.012
GPT teacher head0.309
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2000
Admission routes2
Has abstractyes

Explore more

Same venuePain Research and ManagementSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207