MétaCan
Menu
Back to cohort
Record W2922743160 · doi:10.1016/j.jpain.2019.03.009

A Definition of “Flare” in Low Back Pain: A Multiphase Process Involving Perspectives of Individuals With Low Back Pain and Expert Consensus

2019· article· en· W2922743160 on OpenAlexaff
Nathalia Costa, Manuela L. Ferreira, Jenny Setchell, Joanna Makovey, Tanya Dekroo, Aron Downie, Ashish D. Diwan, Bart W. Koes, Bård Natvig, Bill Vicenzino, David J. Hunter, Eric J. Roseen, Eva Rasmussen-Barr, Françis Guillemin, Jan Hartvigsen, Kim L. Bennell, Leonardo Oliveira Pena Costa, Luciana Macedo, Marina B. Pinheiro, Martin Underwood, Maurits W. van Tulder, Melker S. Johansson, Paul Enthoven, Peter Kent, Peter O’Sullivan, Pradeep Suri, Stéphane Genevay, Paul W. Hodges

Bibliographic record

VenueJournal of Pain · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
FundersNational Health and Medical Research CouncilMedical Research CouncilNational Institute for Health and Care ResearchStrong
KeywordsLow back painProcess (computing)FlareBack painPsychologyPhysical therapyMedicinePhysical medicine and rehabilitationComputer scienceAlternative medicineEngineering

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)0.334
metaresearch head score (Gemma)0.288
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.334
Threshold uncertainty score0.821

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3340.288
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0130.005
Science and technology studies0.0160.021
Scholarly communication0.0170.019
Open science0.0100.033
Research integrity0.0130.022
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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 designQualitative
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

Citations38
Published2019
Admission routes1
Has abstractno

Explore more

Same venueJournal of PainSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207