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Record W2339565516

Validation of the Ottawa ankle rules in a Danish emergency department.

2010· article· en· W2339565516 on OpenAlexaboutno aff
Radu Vijdea, Frank Damborg

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleEmergency departmentReduction (mathematics)RadiographyMalleolusSurgeryPhysical therapyOrthodontics
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The Ottawa ankle rules (OAR) is a tool physicians may use to determine whether or not to perform an x-ray after an ankle or midfoot distortion or blunt trauma to these structures. The rationale of using the OAR is to exclude a fracture by means of clinical examination without resort to x-rays, and thereby limiting the use of x-rays, time, costs, etc. The principle of the OAR is that an ankle x-ray is only required when there is bone tenderness along the distal six centimetres of the posterior part of the medial or lateral malleolus, or when the patient is unable to bear weight immediately after the accident and in the emergency department (ED). Similarly, an x-ray of the midfoot is required only when there is bone tenderness at the base of the 5th metatarsal or the navicular bone, or when the patient is unable to bear weight immediately after the accident and also in the ED. Our hypothesis was that by introducing the OAR, we would reduce the use of x-rays without increasing the number of missed fractures. MATERIAL AND METHODS: The study was designed as an intervention study with 882 patients in the control group and 1,014 patients in the intervention group. The intervention consisted of several OAR implementation measures. RESULTS: Before use of the OAR, 62% had an x-ray taken. This proportion was reduced to 57% with the introduction of the OAR. We registered a significant reduction (p < 0.001) in x-ray without increasing the number of missed fractures. CONCLUSION: The OAR may reduce the number of x-rays and possibly also save costs and time if implemented in Denmark.

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)0.000
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 score0.033
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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