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Record W3026713351

Applicability of Ottawa ankle rules in predicting the need for radiography in ankle and midfoot injuries in Rwanda

2020· article· en· W3026713351 on OpenAlexaboutno aff
Emmanuel Murwanashyaka, A.M. Buteera, J. Byimana, Emmanuel Bukara, Albert Nzayisenga, Jean Claude Byiringiro

Bibliographic record

VenueEast African Orthopaedic Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsAnkleMedicineRadiographyLimitingReferralPhysical therapySurgeryFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: To assess the applicability of Ottawa Ankle Rules in predicting the need for the radiography in ankle and midfoot injuries in Rwanda.Design: This was a prospective multicenter cross-sectional study carried over a 6 month duration, from May 2018 to October 2018.Setting: University Teaching Hospital of Kigali (UTH-Kigali), Rwanda Military Hospital (RMH) and King Faisal Hospital, Kigali, Rwanda (KFH-K).Patients and methods: Adult patients presenting with acute ankle and midfoot injuries at the emergencies of three referral hospitals in Kigali. Patients were examined using OARs and underwent radiography to rule out the presence or absence of the fracture.Results: A total of 196 patients from three referral hospitals in Kigali were enrolled in the study. The sensitivity and specificity of the OARs were 97.9% and 35.8% respectively.Conclusion: In this study, Ottawa Ankle Rules have high sensitivity and low specificity; however, it showed high false positive values due to high sensitivity of the test. When properly applied, Ottawa Ankle Rules can decrease the number of unnecessary ankle or midfoot radiographs and limiting the waiting time in acute settings in Rwanda. Key words: Ottawa ankle rules, Radiography, Applicability, Validation

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)0.002
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 score0.015
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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
Published2020
Admission routes1
Has abstractyes

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