Avulsion fractures of the pelvis - a qualitative systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: To assess a causal relationship between physical activity or boney surgical intervention and the occurrence of avulsion fracture in the pelvis. Secondarily to assess the average age at which avulsion fracture occurs in cases associated with physical activity or boney surgery. METHOD: A literature search was performed on a variety of databases using text words and MeSH terms. Results were limited to English language. Cases involving trauma or pathological disease were excluded. Causation Criteria scores were calculated for each paper to establish a link between the suspected mechanism of injury and avulsion fracture. RESULTS: 48 papers were retrieved encompassing 66 cases of avulsion fracture. 88% of cases were associated with physical activity while 12% were associated with a history of surgery. Average age in the physical activity cases was 16.8(range 13-43) and 56.4(range 31-74) in the surgery related cases. Causation Criteria scores were definite in 76% of activity related cases and probable in 60% of boney surgery related cases. CONCLUSIONS: Avulsion fractures of the pelvis represent a highly prevalent pathology among the adolescent athletic population. A population of skeletally mature patients with history of boney surgical intervention are also at risk.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".