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Record W1976201814 · doi:10.1016/j.jhse.2007.07.001

The Surgical Treatment of Cubital Tunnel Syndrome: A Decision Analysis

2007· article· en· W1976201814 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hand Surgery (European Volume) · 2007
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCubital tunnel syndromeMedicineDecompressionSurgeryCubital tunnelTransposition (logic)Surgical decompressionDecision analysisUlnar nerveStatistics

Abstract

fetched live from OpenAlex

The objective of our study was to use decision analysis to compare four common surgical treatments for cubital tunnel syndrome: simple decompression of the cubital tunnel, medial epicondylectomy, anterior subcutaneous transposition and anterior submuscular transposition. The variables used for this decision analysis model were based on data from the literature. Extensive sensitivity analyses were carried out to test the impact of the values given to these variables on the outcome of the model. The highest expected utility, 0.973, was associated with simple decompression. The expected utility was 0.969 for subcutaneous transposition and 0.965 for submuscular transposition. Medial epicondylectomy had the lowest expected utility at 0.961. Simple decompression remained the preferred strategy in extensive one-way sensitivity analyses.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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