MétaCan
Menu
Back to cohort
Record W4404634487 · doi:10.1016/j.otsr.2024.104054

Does internal fixation of shaft fracture show specificities in over-80 year-olds?

2024· review· en· W4404634487 on OpenAlexaff
G. Piétu

Bibliographic record

VenueOrthopaedics & Traumatology Surgery & Research · 2024
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineInternal fixationSurgeryFracture (geology)

Abstract

fetched live from OpenAlex

Osteoporotic fractures in the elderly are increasingly numerous, but diaphyseal locations on native bone are quite rare. Pathological and periprosthetic fractures are not included in this review, as they are specific in terms of context and treatment. Cortical thinning and widening of the medullary canal alter local mechanical properties, necessitating adaptation of internal fixation. Thus, for nailing, the diameter of the implant has to be greater, and fixed-angle or multidirectional locking screws are used; for plate fixation, locking screws are required. To avoid secondary periprosthetic fracture, fixation must protect the entire bone segment. Long plates should be used, with several divergent epiphyseal end-screws; in the femur, cervicocephalic proximal fixation is recommended. In practice, nailing is mostly used in femoral and tibial isthmic locations. In case of metaphyseal extension, nail and locking plate fixation, ideally percutaneous, show comparable results in terms of function, consolidation and complications. In the tibia, it is mandatory to be soft-tissue friendly given the fragility of pretibial skin in the elderly. In the humerus, the choice is wider. For nailing, passage through the rotator cuff seems acceptable in elderly patients. LEVEL OF EVIDENCE: V; expert opinion.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.104
GPT teacher head0.426
Teacher spread0.322 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOrthopaedics & Traumatology Surgery & ResearchSame topicBone fractures and treatmentsFrench-language works237,207