Approach to traumatic hand injuries for primary care physicians.
Bibliographic record
Abstract
OBJECTIVE: To review the initial management of common traumatic hand injuries seen by primary care physicians. SOURCES OF INFORMATION: Current clinical evidence and literature identified through MEDLINE electronic database searches was reviewed. Expert opinion was used to supplement recommendations for areas with little evidence. MAIN MESSAGE: Primary care physicians must routinely manage patients with acute traumatic hand injuries. In the context of a clinical case, we review the assessment, diagnosis, and initial management of common traumatic hand injuries. The presentation and management of nail bed injuries, fingertip amputations, mallet fingers, hand fractures, tendon lacerations, bite injuries, and infectious tenosynovitis will also be discussed. The principles of managing traumatic hand injuries involve the reduction and immobilization of fractures, obtaining post-reduction x-ray scans, obtaining soft tissue coverage, preventing and treating infection, and ensuring tetanus prophylaxis. CONCLUSION: Proper assessment and management of traumatic hand injuries is essential to prevent substantial long-term morbidity in this generally otherwise healthy population. Early recognition of injuries that require urgent or emergent referral to a hand surgeon is critical.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".