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Record W4294073759 · doi:10.2106/jbjs.cc.22.00251

Management of Patella Fracture Nonunion and Large Bone Defect After Bone–Patellar Tendon–Bone Autograft

2022· article· en· W4294073759 on OpenAlexaff
Justin-Pierre Lorange, Dena Bakhsh, Carl Laverdière, Yousef Marwan, Gregory K. Berry

Bibliographic record

VenueJBJS Case Connector · 2022
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineNonunionPatellaPatella fractureSurgery

Abstract

fetched live from OpenAlex

CASE: A 32-year-old patient was diagnosed with a vertical patella fracture nonunion after a bone-patellar tendon-bone (BTB) anterior cruciate ligament reconstruction. In addition, a 1 × 2 × 1 cm patellar bone defect was noticed at the graft harvesting site. The patient was treated surgically with open reduction and internal fixation and iliac crest bone autograft which resulted in fracture union. CONCLUSION: Many intraoperative and postoperative risk factors for iatrogenic patella fracture when harvesting BTB autograft were identified. Surgeons should be aware of technical skills needed to prevent this complication and should treat the fracture appropriately to avoid nonunion and optimize the outcome.

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)0.002
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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
Published2022
Admission routes1
Has abstractyes

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