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Record W4297816569 · doi:10.3126/jucms.v10i01.47218

Reliability of ‘Ottawa Ankle Rules’ in Acute Ankle and Midfoot Injuries

2022· article· en· W4297816569 on OpenAlexaboutno aff
Abhishek Kumar Thakur, Prakriti Raj Kandel

Bibliographic record

VenueJournal of Universal College of Medical Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleOrthopedic surgeryRadiographyEmergency departmentPhysical therapySurgery

Abstract

fetched live from OpenAlex

INTRODUCTIONThe Ottawa ankle rules (OARs) are clinical decision guidelines used to identify whether patients with ankle injuries need to undergo radiography. The OARs have been proven that their application reduces unnecessary radiography. MATERIAL & METHODSThis prospective study was conducted at Sumeru City Hospital, Lalitpur in the Department of Emergency and Outpatient Department of Orthopaedics. Thirty-six patients were included in the study. Twenty-five patients were in ankle group and 11 patients were in midfoot group. All patients were sent for X-rays after evaluating them according to OARs. RESULTS Among 36 cases, 8 clinically significant fractures were found. Sensitivity of OARs for detecting fractures was 100 % for both ankle and midfoot group. Specificity of OARs for detecting fractures were 47.36 % for ankle group and 66.67 % for midfoot group. Negative predictive value of OARs was 100 %. CONCLUSIONOARs are very accurate and highly sensitive tools for detecting fractures in acute ankle and midfoot injuries. Implementation of these rules would lead to significant reduction in the number of radiographs and thereby reduce the cost of the treatment, radiation exposure and waiting time of patients at hospital.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.294
Teacher spread0.279 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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