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Record W2323702580 · doi:10.1097/bot.0b013e3181fc6255

A Fragment-Specific Approach to Type IID Monteggia Elbow Fracture–Dislocations

2011· article· en· W2323702580 on OpenAlexaff
Daphne M. Beingessner, Sean E. Nork, Julie Agel, Darius G. Viskontas

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2011
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsRoyal Columbian Hospital
Fundersnot available
KeywordsMedicineHeterotopic ossificationElbowTrauma centerSurgeryRadiographyPalsyInternal fixationRadial head fractureMuscle contractureBone healingRange of motionReduction (mathematics)OssificationRetrospective cohort studyRadial head

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe the pattern of injury, surgical technique, and outcomes of Monteggia Type IID fracture dislocations. DESIGN: Retrospective review of prospectively collected clinical and radiographic patient data in an orthopaedic trauma database. SETTING: Level I university-based trauma center. PATIENTS/PARTICIPANTS: All patients with Monteggia Type IID fracture-dislocations admitted from January 2000 to July 2005. INTERVENTION: Review of patient demographics, fracture pattern, method of fixation, complications, additional surgical procedures, and clinical and radiographic outcome measures. MAIN OUTCOME MEASUREMENTS: Clinical outcomes: elbow range of motion, complications. Radiographic outcomes: characteristic fracture fragments, quality of fracture reduction, healing time, degenerative changes, and heterotopic ossification. RESULTS: Sixteen patients were included in the study. All fractures united. There were six complications in six patients, including three contractures with associated heterotopic ossification, one pronator syndrome and late radial nerve palsy, one radial head collapse, and one with prominent hardware. CONCLUSIONS: Monteggia IID fracture-dislocations are complex injuries with typical specific fracture fragments. Anatomic fixation of all injury components and avoidance of complications where possible can lead to a good outcome in these challenging injuries.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.727

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 head0.058
GPT teacher head0.271
Teacher spread0.213 · 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

Citations27
Published2011
Admission routes1
Has abstractyes

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