Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".