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Record W2161271928

Scaphoid fracture. Review of diagnostic tests and treatment.

2000· article· en· W2161271928 on OpenAlexaff
H Schubert

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScaphoid fractureMedicineScaphoid boneThumbCarpal bonesPhysical examinationFracture (geology)WristObservational studyBone scintigraphyRadiologySurgery
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To help make diagnosis and treatment of scaphoid fracture more precise by review of published evidence. QUALITY OF EVIDENCE: MEDLINE was searched using the terms "scaphoid," "carpal navicular," "fracture," "computed tomography," "bone scan," and "scintigraphy." Most papers were case-series observational reports. Papers were cited if the case series was large or if there was a high degree of agreement among several observers. The main recommendation for change in treatment of scaphoid fracture is based on two randomized clinical trials involving more than 1000 patients with proven scaphoid fracture. MAIN MESSAGE: Fracture of the scaphoid requires a specific mechanism of injury. "Snuffbox" tenderness is not specific for scaphoid fracture and is not the most useful physical finding; other physical findings provide more specific evidence for or against scaphoid fracture. Physical examination remains the basis of initial treatment and should be thorough and meticulous. X-ray films must be of high quality and should be examined carefully for bone and soft tissue signs of fracture. A Colles'-type short arm cast is adequate for treating common undisplaced scaphoid waist fractures; the thumb need not be immobilized. For suspected scaphoid fractures, without radiologic evidence of fracture, treating symptoms is likely sufficient. CONCLUSION: Evidence found in the literature can be used to improve diagnostic accuracy for scaphoid fractures, to optimize treatment for these injuries, and to reduce unnecessary immobilization and disability for patients.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.250
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations34
Published2000
Admission routes1
Has abstractyes

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