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Record W3135808478

An Overview of Systematic Reviews on Prognostic Factors in Neck Pain

2013· article· en· W3135808478 on OpenAlexfundno aff
David M. Walton, Linda Carroll, Helge Kasch, Michele Sterling, Arianne Verhagen, Joy C. MacDermid, Anita Gross, Lina Santaguida, Lisa C. Carlesso

Bibliographic record

VenueRePub (Erasmus University, Rotterdam) · 2013
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsMedicineNeck painSystematic reviewConfidence intervalMeta-analysisPhysical therapyData extractionBaseline (sea)MEDLINEAlternative medicineInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Given the challenges of chronic musculoskeletal pain and disability, establishing a clear prognosis in the acute stage has become increasingly recognized as a valuable approach to mitigate chronic problems. Neck pain represents a condition that is common, potentially disabling, and has a high rate of transition to chronic or persistent problems. As a field of research, prognosis in neck pain has stimulated several empirical primary research papers, and a number of systematic reviews. As part of the International Consensus on Neck (ICON) project, we sought to establish the general state of knowle

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.111
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0240.022
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
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 head0.056
GPT teacher head0.301
Teacher spread0.245 · 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 designSystematic review
DomainMethods
GenreReview

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

Citations1
Published2013
Admission routes1
Has abstractyes

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