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Record W2170706833 · doi:10.1136/bmj.332.7556.1463

The diagnosis and treatment of carpal tunnel syndrome

2006· letter· en· W2170706833 on OpenAlexaff
Brent Graham

Bibliographic record

VenueBMJ · 2006
Typeletter
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsCarpal tunnel syndromeMedicineSurgical decompressionDecompressionOutcome (game theory)SurgeryCarpal tunnel releaseMedian nerve

Abstract

fetched live from OpenAlex

Surgery—whether open or closed—works, but only if the diagnosis is right The randomised controlled trial of Atroshi and colleagues (p 1473) in this week's BMJ shows that there are no substantive differences in the outcome of carpal tunnel syndrome treated with either a conventional open decompression of the median nerve or an endoscopic approach.1 Their findings confirm those of earlier studies which also found no fundamental difference in outcome that could be attributed to the technique of surgical release of the carpal tunnel.2–4 Given that the result of surgical treatment for carpal tunnel syndrome is not universally successful, however, what other factors might have an important impact on the outcome? One key determinant is probably the accuracy of the diagnosis.5 6 When …

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)0.002
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0070.004

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 head0.030
GPT teacher head0.295
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations12
Published2006
Admission routes1
Has abstractyes

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