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Record W4383033490 · doi:10.1016/j.neuchi.2023.101460

Understanding how the research question impacts trial design: Examples from discectomy trials

2023· review· en· W4383033490 on OpenAlexaff
Jean Raymond, Vivien Kin Yi Chan, Tim E. Darsaut

Bibliographic record

VenueNeurochirurgie · 2023
Typereview
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of Alberta HospitalHealth Sciences CentreCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineClinical trialDiscectomyMEDLINESurgeryInternal medicinePolitical science

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)0.414
metaresearch head score (Gemma)0.661
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.586
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4140.661
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0030.005
Science and technology studies0.0010.006
Scholarly communication0.0080.009
Open science0.0040.004
Research integrity0.0100.011
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 head0.884
GPT teacher head0.551
Teacher spread0.333 · 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 designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractno

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