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Record W3125362987 · doi:10.1097/oi9.0000000000000006

Direct visualization of the syndesmosis for evaluation of syndesmotic disruption

2018· article· en· W3125362987 on OpenAlexaff
Nayla Gosselin-Papadopoulos, Jonah Hébert‐Davies, G. Yves Laflamme, Jérémie Ménard, Stéphane Leduc, Dominique M. Rouleau, Marie‐Lyne Nault

Bibliographic record

VenueOTA International The Open Access Journal of Orthopaedic Trauma · 2018
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsSyndesmosisDiastasisCadaveric spasmMedicineFluoroscopyCadaverAnkleOrthodonticsRadiologyAnatomySurgeryTibiaFibula

Abstract

fetched live from OpenAlex

OBJECTIVES: Radiologic criteria for syndesmosis instability evaluation remain controversial and direct visualization (DV) of the distal tibiofibular articulation is an alternative diagnostic method worthy of further investigation. We speculate that DV is a more accurate way to evaluate syndesmosis instability than fluoroscopy. The purpose of this study is to determine whether syndesmosis instability can accurately be recognized through DV and if this new intraoperative diagnostic method is more sensitive than fluoroscopy in detecting syndesmosis instability. METHODS: Ten cadaveric ankles were tested using a sequential iatrogenic syndesmosis injury model. Specimens were tested incrementally with the lateral stress test (LST) and the external rotation stress test (ERT). The resulting instability was measured directly and fluoroscopically with a true mortise view by using medial clear space (MCS) and tibiofibular clear space (TFCS). RESULTS: = 0.0077) with the LST and ERT, respectively. Fluoroscopically, TFCS showed a significant diastasis only with a complete syndesmosis rupture while MCS did not show any significant differences. CONCLUSIONS: DV of the syndesmosis in a cadaver injury model appears to be more sensitive than fluoroscopy in identifying injury, especially incomplete syndesmotic disruption.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.349
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.133
GPT teacher head0.457
Teacher spread0.324 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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