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Record W2807969045 · doi:10.1097/md.0000000000011020

The heterogeneous management of pediatric ankle traumas

2018· article· en· W2807969045 on OpenAlexaff
Philippe Voizard, James Moore, Stéphane Leduc, Marie‐Lyne Nault

Bibliographic record

VenueMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsCegep Edouard MontpetitCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineAnkleOrthopedic surgeryPediatric traumaRetrospective cohort studyInclusion and exclusion criteriaPhysical therapySurgeryEmergency medicineInjury preventionPoison control

Abstract

fetched live from OpenAlex

Frequent misdiagnosis of pediatric ankle traumas leads to inappropriate management, which may result in residual pain, instability, slower return to physical activity, and long-term degenerative changes. The purpose of this study was to evaluate the consistency of diagnosis, management, and the treatment of acute lateral pediatric ankle trauma in a tertiary care pediatric hospital. The hypothesis was that the initial diagnosis is often incorrect, and the treatment varies considerably amongst orthopedic surgeons.We conducted a retrospective study of all cases of ankle sprains and Salter-Harris one (SH1) fractures referred to our orthopedic surgery service between May and August 2014. Exclusion criteria included ankle fractures other than SH1 types, and cases where treatment was initially undertaken elsewhere before referral to our service. Primary outcome was the difference between initial and final diagnosis.Among 3047 cases reviewed, 31 matched our inclusion criteria. Initial diagnosis was 20 SH1 fractures, 8 acute ankle sprains, and 3 uncertain, with a change in diagnosis for 48.5% at follow-up.Accurate diagnosis can be difficult in pediatric ankle trauma, with case management and specific treatments varying considerably. This study reinforces the need to evaluate the safety of a general treatment algorithm for all lateral ankle trauma with normal radiographs.Level of evidence III.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.378

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 head0.030
GPT teacher head0.291
Teacher spread0.260 · 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 designNot applicable
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

Citations9
Published2018
Admission routes1
Has abstractyes

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