MétaCan
Menu
← Back to cohort
Record W7127986615 · doi:10.4103/juoa.juoa_10_24

Successful Management of a Vancouver Type C Periprosthetic Femur Fracture in a Nonagenarian: Surgical Challenges and Review of Current Concepts

2023· article· en· W7127986615 on OpenAlexaboutno aff
Anil Regmi, Jhapindra Pokharel, Pradeep Kafle

Bibliographic record

VenueJournal of the Uttaranchal Orthopaedic Association · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsPeriprostheticFemurReduction (mathematics)Internal fixationFixation (population genetics)Femur fractureArthroplasty

Abstract

fetched live from OpenAlex

Abstract Periprosthetic femur fractures are complex and challenging to manage, especially in elderly patients. Vancouver Type C fractures, which occur distal to the femoral stem, pose significant surgical challenges due to the need for stable fixation in often osteoporotic bone. This article describes the management of a 91-year-old female with a Vancouver Type C periprosthetic femur fracture treated with open reduction and plating. A 91-year-old female presented with a periprosthetic femur fracture classified as Vancouver Type C. She underwent open reduction and internal fixation with plating. Despite her advanced age, significant comorbidities, and severe osteoporosis, she demonstrated good postoperative recovery, with bone healing observed at follow-up visits. Managing periprosthetic femur fractures in elderly patients requires careful surgical planning and consideration of the patient’s overall health and bone quality. This case highlights the feasibility and effectiveness of open reduction and plating in achieving satisfactory outcomes for Vancouver Type C fractures in very elderly patients.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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 designCase report
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Uttaranchal Orthopaedic Association→Same topicOrthopaedic implants and arthroplasty→French-language works237,207→