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Record W1914944146

The Ottawa ankle rules for the use of diagnostic X-ray in after hours medical centres in New Zealand.

2002· article· en· W1914944146 on OpenAlexaboutno aff
Simon Wynn-Thomas, Tom Love, Deborah McLeod, Sue Vernall, Marjan Kljakovic, Antony Dowell, John Durham

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleReduction (mathematics)JudgementPrimary careClinical judgementPhysical therapySurgeryEmergency medicineFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

AIMS: The aims of this study were to measure baseline use of Ottawa ankle rules (OAR), validate the OAR and, if appropriate, explore the impact of implementing the Rules on X-ray rates in a primary care, after hours medical centre setting. METHODS: General practitioners (GPs) were surveyed to find their awareness of ankle injury guidelines. Data concerning diagnosis and X-ray utilisation were collected prospectively for patients presenting with ankle injuries to two after hours medical centres. The OAR were applied retrospectively, and the sensitivity and specificity of the OAR were compared with GPs clinical judgement in ordering X-rays. The outcome measures were X-ray utilisation and diagnosis of fracture. RESULTS: Awareness of the OAR was low. The sensitivity of the OAR for diagnosis of fractures was 100% (95% CI: 75.3 - 100) and the specificity was 47% (95% CI: 40.5 - 54.5). The sensitivity of GPs clinical judgement was 100% (95% CI: 75.3 - 100) and the specificity was 37% (95% CI: 30.2 - 44.2). Implementing the OAR would reduce X-ray utilisation by 16% (95% CI: approx 10.8 - 21.3). CONCLUSIONS: The OAR are valid in a New Zealand primary care setting. Further implementation of the rules would result in some reduction of X-rays ordered for ankle injuries, but less than the reduction found in previous studies.

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)0.007
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 score0.283
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.037
GPT teacher head0.232
Teacher spread0.195 · 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

Citations18
Published2002
Admission routes1
Has abstractyes

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