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Record W4391147924 · doi:10.2139/ssrn.4696882

Hierarchy of Patient-Reported Outcome Measures in Randomized Controlled Trials for Chronic Non-Specific Low Back Pain: A Meta-Epidemiological Study – Study Protocol

2024· preprint· en· W4391147924 on OpenAlexaff
Kasper Bülow, Tobias Petra Haugegaard Hørlück, Anders Falk Brekke, Jan Hartvigsen, Jill A. Hayden, Alice Kongsted, Carsten Bogh Juhl

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEpidemiologyRandomized controlled trialMedicineProtocol (science)Meta-analysisOutcome (game theory)Physical therapyChronic painHierarchyLow back painAlternative medicineInternal medicinePathology

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad)
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.080
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.976
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.127
Meta-epidemiology (narrow)0.0080.006
Meta-epidemiology (broad)0.0240.038
Bibliometrics0.0070.006
Science and technology studies0.0030.005
Scholarly communication0.0060.004
Open science0.0040.005
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0430.004

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 head0.111
GPT teacher head0.406
Teacher spread0.296 · 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

Labeled directly by 2 models reading the full record.

MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
DomainMethods
GenreProtocol

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 routes1
Has abstractno

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