Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it