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Record W2072388407 · doi:10.1007/s00167-009-0930-x

PCL reconstruction with the tibial inlay technique following intra‐medullary nail fixation of an ipsilateral tibial shaft fracture: a treatment algorithm

2009· article· en· W2072388407 on OpenAlexaff
Jaskarndip Chahal, Herman S. Dhotar, Ali Zahrai, Daniel B. Whelan

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2009
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsIntramedullary rodMedicineInlayFixation (population genetics)Medullary cavityTibial fractureLigamentSurgeryTibiaOrthodonticsAlgorithmComputer scienceDentistryAnatomy

Abstract

fetched live from OpenAlex

Our case report highlights the complexity of treating multi-ligament knee injuries in the setting of ipsilateral long bone trauma. We describe the use of the tibial inlay technique for PCL reconstruction in the setting of a tibial shaft fracture treated with an intramedullary nail. We also present a comprehensive treatment algorithm for the treatment of ligamentous knee injuries in the setting of long bone trauma.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0050.003
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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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
GenreMethods

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

Citations6
Published2009
Admission routes1
Has abstractyes

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