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Record W2484950710 · doi:10.1002/9781119413936.ch98

Subtrochanteric Femur Fractures

2021· other· en· W2484950710 on OpenAlexaff
John Morellato, Steven Papp, Wade Gofton, Allan Liew

Bibliographic record

VenueEvidence-Based Orthopedics · 2021
Typeother
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsIntramedullary rodMalunionNonunionFemurMedicineReduction (mathematics)SurgeryGreater trochanterLesser TrochanterDynamic hip screwImplantOrthodonticsMathematics

Abstract

fetched live from OpenAlex

This chapter presents a case scenario of a 45-year-old obese patient who is otherwise well presents to the Emergency Department after a motor vehicle accident. X-rays show a proximal femur fracture. When performing intramedullary nailing of subtrochanteric fractures, there has been debate over different aspects of the surgical technique including the starting point. The insertion site for anterograde nailing of subtrochanteric femur fractures can be located in the piriformis fossa or the tip of the trochanter. Malreduction of subtrochanteric fractures increases implant failure rates, increases malunion rates, and increases nonunion rates. In subtrochanteric femur fractures, the personality of the fracture is determined by the deforming forces of muscular action on the proximal and distal fragments and closed reduction is often unable to achieve a satisfactory reduction. The chapter also provides recommendations for implementing evidence-based practice in the clinical setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.050
GPT teacher head0.341
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

Explore more

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