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Using GRADE for evaluating the quality of evidence in hyperbaric oxygen therapy clarifies evidence limitations

2013· review· en· W2087477832 on OpenAlexaff
M. Hassan Murad, Osama Altayar, Michael H Bennett, Justin Wei, Paul Claus, Noor Asi, Larry J. Prokop, Víctor M. Montori, Gordon Guyatt

Bibliographic record

VenueJournal of Clinical Epidemiology · 2013
Typereview
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHyperbaric oxygenMedicineDecompression illnessObservational studyQuality of evidenceRandomized controlled trialEvidence-based medicineGrading (engineering)Carbon monoxide poisoningIntensive care medicinePhysical therapyDecompressionPoison controlSurgeryDecompression sicknessAlternative medicineEmergency 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

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)0.203
metaresearch head score (Gemma)0.464
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.797
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2030.464
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0240.049
Bibliometrics0.0290.019
Science and technology studies0.0020.005
Scholarly communication0.0110.008
Open science0.0060.006
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.976
GPT teacher head0.723
Teacher spread0.254 · 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
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

Citations23
Published2013
Admission routes1
Has abstractno

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