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Record W3024897686

Secondary femur fracture following treatment with anterograde nailing: the state of the art.

2019· article· en· W3024897686 on OpenAlexaboutno aff
G Cazzato, Giulia Masci, Francesco Liuzza, Letizia Capasso, Michela Florio, Carlo Perisano, Raffaele Vitiello, Gianluca Ciolli, Giulio Maccauro

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsIntramedullary rodMedicineComplicationFemurSurgeryRadiographyFemur fractureBone healingFracture (geology)Incidence (geometry)
DOInot available

Abstract

fetched live from OpenAlex

Cephalomedullary nailing (CMN) currently represents the best surgical technique for the treatment of intertrochanteric hip fractures. Although the success of CMN in terms of functional recovery and fracture healing, in clinical practice there are many complications. Later femur fracture following treatment of trochanteric fracture with CMN is not a very frequent complication but, when it occurs, its treatment is the most complex, because of the increase of peri-operative mortality. There are studies in literature, which have demonstrated that the incidence of this complication is about 0.5-3%. Diagnosis and classification are made with standard radiographs, using the AO classification and the modified Vancouver classification. In the actual literature, to determinate the predisposing factor to the secondary fractures, the authors focused their attention on patient-related and surgical related risk factors. The treatment is variable and it depends on the type and characteristics of fracture and device. Outcomes analyzed in literature were mortality and bone healing. The aim of this manuscript is to provide an overview of this topic and to describe the state of the art of the secondary fracture after surgical treatment with intramedullary nailing.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.223

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 head0.009
GPT teacher head0.217
Teacher spread0.207 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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