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Record W4399739502 · doi:10.1080/24740527.2024.2346253

Mission Critical, a Call to Action for Implementation of the Recommendations of the Canadian Pain Task Force

2024· editorial· en· W4399739502 on OpenAlexaffabout
Mary Lynch

Bibliographic record

VenueCanadian Journal of Pain · 2024
Typeeditorial
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaDalhousie University
Fundersnot available
KeywordsCall to actionTask forceTask (project management)Action (physics)Physical medicine and rehabilitationPsychologyMedicinePolitical scienceBusinessPublic administrationEconomicsManagementMarketingPhysics

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)0.023
metaresearch head score (Gemma)0.098
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.986
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.098
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0060.003
Science and technology studies0.0080.007
Scholarly communication0.0160.006
Open science0.0070.002
Research integrity0.0450.056
Insufficient payload (model declined to judge)0.0130.010

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 head0.020
GPT teacher head0.365
Teacher spread0.345 · 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
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

Citations0
Published2024
Admission routes2
Has abstractno

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