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Record W3198018346 · doi:10.23750/abm.v92i4.9962

Accuracy of Ottawa ankle rules for midfoot and ankle injuries.

2021· article· en· W3198018346 on OpenAlexaboutno aff
Selen Yavas, Engin Deniz Arslan, Seda Özkan, Yasemin Yılmaz Aydın, Macit Aydın

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsAnkleMedicineRadiographyFoot (prosody)Incidence (geometry)Prospective cohort studySurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Background The management of ankle sprains is common practice in emergency departments. Traditionally, physicians would order radiographs for all ankle injuries although the overall incidence of fractures are less than 15% . The Ottawa Ankle Rules (OAR) have been developed to predict the necessity of radiographs in acute ankle injuries. Material and Method This is a prospective study of consecutive patients aged 16 years or older with acute non-penetrating ankle or foot injuries and who had a radiography of ankle or foot or both. Results 499 cases were included in the study. 56.90 % of the patients were male and the median age of the patients was 30 (IQR 22,44). 22.85 % (114/499) of patients with ankle or midfoot injuries had fractures. The sensitivity, specificity, PPV and NPV of OAR for ankle and midfoot injuries were 100, 45.26, 26.00, 100 and 100, 43.71, 19.92 and 100 respectively. In this study 792 x rays were ordered from 499 patients. According to OAR 509 (%64.27) of them were indicated whereas 283 (% 35.73) were not. When the weight bearing test is sole criteria 303 ( 38.26%) x rays were obtained to find out three fractures. Conclusion OAR should be safely used in emergency departments. Implantation of this rule prevents patients from unnecessary radiation exposure. It is a reasonable approach to reassess the patient if symptoms not resolve several days later for avoiding unnecessary x ray exposure when the weight bearing test exist as the only positive criteria.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.263
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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