A Narrative Review on Avulsion Fractures of the Upper and Lower Limbs
Bibliographic record
Abstract
Avulsion fractures compromise function and movement at the affected joint. If left untreated, it can lead to deformity, nonunion, malunion, pain, and disability. The purpose of this review was to identify and describe the epidemiology and available treatment options for common avulsion fractures of the upper and lower extremities. Current evidence suggests that optimal treatment is dependent on the severity of the fracture. Conservative efforts generally include casting or splinting with a period of immobilization. Surgery is typically indicated for more severe cases or if nonoperative treatments fail; patient demographics or preferences and surgeon experience may also play a role in decision making. Some avulsion fractures can be surgically managed with any one of various techniques, each with their own pros and cons, and often there is no clear consensus on choosing one technique over another; however, there is some research suggesting that screw fixation, when possible, may offer the best stability and compression at the fracture site and earlier mobilization and return to function. Physicians should be mindful of the potential complications associated with each intervention.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".