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Record W4327683225 · doi:10.26355/eurrev_202303_31548

Comparison of Shetty ankle test and Ottawa ankle rules in ankle injuries.

2023· article· en· W4327683225 on OpenAlexaboutno aff
Öner Avınca, Mahmut Taş

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsAnkleTest (biology)MedicineFoot (prosody)Accident and emergencyPhysical therapyOrthopedic surgeryMedical emergencySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: Foot and ankle injuries are the most common extremity trauma and have an important place among the admissions to the emergency service. Ankle injuries are the most frequent form of orthopedic emergencies. Currently, Ottawa ankle rules (ORL) is the most popular test used worldwide for the ankle injuries. Shetty test is an important test used to evaluate the clinical status of patients admitted for the ankle trauma. PATIENTS AND METHODS: This study was initiated in the emergency service between May 1, 2018, and May 1, 2019, after the approval of the Ethics Committee. The patients were classified according to gender, age, and admission, Ottawa test results, Shetty test results, and radiography status. RESULTS: Shetty test can be used to differentiate between the foot sprains and fractures as a simple and inexpensive method. Despite its effectiveness, Ottawa ankle rules is not widely used due to the detailed implementation of the Ottawa ankle rules and the components of the test. The ease of applicability of the Shetty test in primary care makes the test stand out. In our study, although the sensitivity of Shetty and Ottawa tests resulted in close percentages (82.86% - 85.71%), the specificity of Ottawa test was found to be higher. CONCLUSIONS: Detecting a fracture in emergency services is more valuable than excluding a fracture. Even though Shetty test is simple to use, we recommend Ottawa test for the foot and ankle sprains due to the legal problem that could be caused by Shetty test.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.292
Teacher spread0.252 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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