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Record W7094296462

Audit on the use of radiography and the management of ankle sprains in A&E

2008· article· en· W7094296462 on OpenAlexaboutno aff

Bibliographic record

VenueOAR@UM (University of Malta) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsAnkleFoot (prosody)AuditRadiographyGuidelinePhysical examinationEmergency departmentClinical Practice
DOInot available

Abstract

fetched live from OpenAlex

Objective: To determine the need for a local implementation strategy of the Ottawa Ankle and Foot rule in the A& E Department of St. Luke' s Hospital, Malta and to examine the current management practices of ankle sprains. Methods: A prospective study was conducted on all patients aged 16 years and over presenting to the A& E department of St. Luke’s Hospital, Malta over a six week period with ankle and midfoot injuries. Data collected included time and mechanism of injury, clinical examination findings, radiographic investigations ordered, management and disposal of patient. Results: Sixty nine (95%) of the 73 patients presenting with ankle injuries underwent x-ray investigation. In total 90 x-ray series (i.e. AP and lateral) were performed, 62 of which were ankle x-rays and 28 were foot x-rays. Clinical application of the Ottawa ankle rules (OAR) would have resulted in a reduction of ankle x-rays by 19.4% and foot x-rays by 32.1%. Management of severe ankle sprains included written discharge instructions in 54% and referrals to physiotherapy in 31%. One patient out of the 13 with severe ankle sprains was given a follow up appointment at Fresh Trauma Clinic (FTC). Conclusion: A local implementation strategy for the OAR in the A& E department may result in an overall reduction in radiographic requests by 21%. An appropriate guideline for management of ankle injuries incorporating the OAR is needed and should be developed using the available evidence base.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.999

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 studies0.0000.003
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 head0.054
GPT teacher head0.234
Teacher spread0.180 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2008
Admission routes1
Has abstractyes

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