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Record W1608764155 · doi:10.1007/s12630-010-9306-4

The Canadian STOP-PAIN project – Part 2: What is the cost of pain for patients on waitlists of multidisciplinary pain treatment facilities?

2010· article· en· W1608764155 on OpenAlexafffundabout
Denise N. Guerriere, Manon Choinière, Dominique Dion, Philip Peng, Emma Stafford-Coyte, Brandon Zagorski, Robert Banner, Pamela M. Barton, Alexander J. Clark, Allan Gordon, Marie‐Claude Guertin, Howard Intrater, Sandra LeFort, Mary Lynch, Dwight E. Moulin, May Ong-Lam, Mélanie Racine, Saifee Rashiq, Yoram Shir, Paul Taenzer, Mark A. Ware

Bibliographic record

VenueCanadian Journal of Anesthesia/Journal canadien d anesthésie · 2010
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversité du Québec à MontréalSt. Paul's HospitalLondon Health Sciences CentreMemorial University of NewfoundlandHealth Sciences CentreMount Sinai HospitalAlberta Health ServicesCentre Hospitalier de l’Université de MontréalMcGill University Health CentreInstitute for Clinical Evaluative SciencesQueen Elizabeth II Health Sciences CentreUniversity of AlbertaUniversité de MontréalUniversity of ManitobaMontreal Heart InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchPfizer CanadaPfizer
KeywordsActivity-based costingMedicineMultidisciplinary approachHealth careQuality of life (healthcare)Chronic painEconomic evaluationTotal costBusinessPhysical therapyNursingEconomicsMarketing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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 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

Citations103
Published2010
Admission routes3
Has abstractno

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