Bibliographic record
Abstract
OBJECTIVES: To report the rates of osteonecrosis and subtalar arthritis after talar neck fractures and to examine if rates have changed over time. DATA SOURCES: A systematic review and meta-analysis of the English literature was performed using EMBASE, MEDLINE, CENTRAL, and Cochrane in November 2011 and updated in November 2014. STUDY SELECTION: Inclusion criteria were studies examining talar neck fractures that reported talar body osteonecrosis rates as a primary or secondary outcome. Exclusion criteria included case series with <10 patients or >50% pediatric patients, inability to isolate results of talar neck fractures, primary treatment of talar excision or arthrodesis, mean follow-up of <3 months, and non-English literature. DATA EXTRACTION: Basic information was collected including journal, author, year published, level of evidence, number of fractures, and follow-up length. Specific information collected included fracture classifications, timing of interventions, method of treatment, osteonecrosis rates, subtalar arthrosis rates, and method of diagnosis of osteonecrosis. DATA SYNTHESIS: Fixed-effects models were used for meta-analysis. The overall event rate of osteonecrosis was calculated and stratified based on Hawkins classification of the talar neck fractures. Mean rates of subtalar arthritis were calculated for all studies and for studies including >2 years of follow-up. CONCLUSIONS: The overall rate of osteonecrosis was 0.312. Rates for Hawkins' types I-IV were 0.098, 0.274, 0.534, and 0.480, respectively. The mean rate of subtalar arthritis was 0.49 but increased to 0.81 in studies with >2 years of follow-up. Complication rates are high in talar neck fractures, and patients should be counseled accordingly.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".