Rendimiento diagnóstico de las reglas de Ottawa para fracturas en lesiones agudas del tobillo en el Hospital Belén de Trujillo.
Bibliographic record
Abstract
OBJECTIVES: To determine the diagnostic performance of the rules of Ottawa for fractures in acute ankle injuries in the Bethlehem Hospital of Trujillo. MATERIALS AND METHODS: We conducted an observational, cross-sectional diagnostic tests that evaluated 114 patients with acute ankle injury, which were divided into two groups at the end of the study; Group I (40 patients with ankle fracture) and Group II (74 patients without ankle fracture). RESULTS: The overall mean age of the sample was 36,05 ± 16,25 years, the mean for the group I was 42,23 ± 18,24 years and for group II was 32,72 ± 14,10 (p 0,05). Regarding the mechanism of injury, in group I predominated falls by 50% and in group II this mechanism was present in 39,19% (p < 0,01), the mean disease duration in group I was 11,90 ± 16,11 and in group II 7,06 ± 7,78 (p < 0,05). By relating the rules of Ottawa ankle fracture and on through the x-ray, it was found that in group I the rules of Ottawa ankle fracture was diagnosed in 100% and in group II was 64,86% (p < 0,001 ). Regarding the performance of the rules of Ottawa, to predict fractures ankle, had to sensitivity, specificity, PPV and NPV were 100%, 35.14%, 45.45% and 100% respectively. CONCLUSIONS: Ottawa rules constitute a diagnostic tool with 100% sensitivity in predicting ankle fracture.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".