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Record W1954611051 · doi:10.1177/03635465000280061501

A Prospective Study of Modified Ottawa Ankle Rules in a Military Population

2000· article· en· W1954611051 on OpenAlexaboutno aff
Barbara A. Springer, Robert A. Arciero, Joachim J. Tenuta, Dean C. Taylor

Bibliographic record

VenueThe American Journal of Sports Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsAnkleMedicineFoot (prosody)RadiographyPhysical therapyOrthopedic surgeryCohen's kappaPopulationPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

To determine the necessity of ankle and foot radiographs, we used modified Ottawa Ankle Rules to evaluate all cadets seen with an acute ankle or midfoot injury at the United States Military Academy. This scoring system determines the need for radiographs. Each patient was independently examined and the decision rules were applied by a physical therapist and an orthopaedic surgeon. Ankle and foot radiographs were obtained for all subjects. Sensitivity, specificity, and the positive predictive value were calculated in 153 patients. There were six clinically significant ankle fractures and three midfoot fractures, for a total incidence of 5.8%. For physical therapists, the sensitivity was 100%, the specificity for ankle injuries was 40%, and the specificity for foot injuries was 79%. For orthopaedic surgeons, the sensitivity was also 100%, the specificity for ankle injuries was 46%, and the specificity for foot injuries was 79%. Interobserver agreement between the orthopaedic surgeons and physical therapists regarding the overall decision to obtain radiographs was high, with a kappa coefficient value of 0.82 for ankle injuries and 0.88 for foot injuries. There were no false-negative results. Use of the modified Ottawa Ankle Rules would have reduced the necessity for ankle and foot radiographs by 46% and 79%, respectively.

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)0.000
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 score0.176
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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

Citations40
Published2000
Admission routes1
Has abstractyes

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