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Record W1595322183 · doi:10.1016/j.spinee.2015.05.018

Strategies to improve the credibility of meta-analyses in spine surgery: a systematic survey

2015· review· en· W1595322183 on OpenAlexafffund
Nathan Evaniew, Leon van der Watt, Mohit Bhandari, Michelle Ghert, Ilyas Aleem, Brian Drew, Gordon Guyatt

Bibliographic record

VenueThe Spine Journal · 2015
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineCredibilityMeta-analysisSPINE (molecular biology)Physical therapyInternal medicineBioinformatics

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)0.624
metaresearch head score (Gemma)0.913
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.376
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6240.913
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0240.032
Bibliometrics0.0260.018
Science and technology studies0.0020.006
Scholarly communication0.0130.017
Open science0.0080.009
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.952
GPT teacher head0.650
Teacher spread0.302 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainEvaluation
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

Citations18
Published2015
Admission routes2
Has abstractno

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