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Record W3152624348 · doi:10.1016/j.jpain.2021.03.147

Non-Surgical Interventions for Lumbar Spinal Stenosis Leading To Neurogenic Claudication: A Clinical Practice Guideline

2021· article· en· W3152624348 on OpenAlexafffund
André Bussières, Carol Cancelliere, Carlo Ammendolia, Christine Comer, Fadi Al Zoubi, Claude-Édouard Châtillon, Greg Chernish, James M. Cox, Jordan A. Gliedt, Danielle Haskett, Rikke Krüger Jensen, Andrée-Anne Marchand, Christy Tomkins‐Lane, Julie O’Shaughnessy, Steven Passmore, Michael Schneider, Peter Shipka, Gregory W. Stewart, Kent Stuber, Albert Yee, Joseph Ornelas

Bibliographic record

VenueJournal of Pain · 2021
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsAlberco Construction (Canada)Research ManitobaUniversity of ManitobaWilliam Osler Health SystemCanadian Memorial Chiropractic CollegeBone and Joint CanadaGDG EnvironnementUniversité du Québec à Trois-RivièresMount Royal UniversityOntario Tech UniversityCentre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-QuébecMcGill UniversityUniversity of TorontoMount Sinai HospitalUniversité du Québec à Montréal
FundersNational Institute for Health and Care ResearchUniversity of Ontario Institute of TechnologyMcGill University
KeywordsMedicineNeurogenic claudicationPhysical therapyGuidelineRandomized controlled trialPopulationLow back painQuality of life (healthcare)Manual therapyEvidence-based practiceRehabilitationPhysical medicine and rehabilitationLumbar spinal stenosisEvidence-based medicineSystematic reviewMEDLINELumbarSurgeryAlternative medicineNursing

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)0.002
metaresearch head score (Gemma)0.007
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: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.003

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 head0.106
GPT teacher head0.485
Teacher spread0.379 · 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
GenreMethods

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

Citations100
Published2021
Admission routes2
Has abstractno

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