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Record W1952494750 · doi:10.2174/1874325001509010463

Acute Osteochondral Fractures in the Lower Extremities - Approach to Identification and Treatment

2015· article· en· W1952494750 on OpenAlexaff
M. Elizabeth Pedersen, Mark P. DaCambra, Zaid Jibri, Samjot S. Dhillon, Ho Jen, Nadr M. Jomha

Bibliographic record

VenueThe Open Orthopaedics Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineOsteoarthritisSurgeryAcute painIntensive care medicineAnesthesiaPathology

Abstract

fetched live from OpenAlex

Chondral and osteochondral fractures of the lower extremities are important injuries because they can cause pain and dysfunction and often lead to osteoarthritis. These injuries can be misdiagnosed initially which may impact on the healing potential and result in poor long-term outcome. This comprehensive review focuses on current pitfalls in diagnosing acute osteochondral lesions, potential investigative techniques to minimize diagnostic errors as well as surgical treatment options. Acute osteochondral fractures are frequently missed and can be identified more accurately with specific imaging techniques. A number of different methods can be used to fix these fractures but attention to early diagnosis is required to limit progression to osteoarthritis. These fractures are common with joint injuries and early diagnosis and treatment should lead to improved long term outcomes.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.050
GPT teacher head0.285
Teacher spread0.235 · 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
GenreOther

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

Citations20
Published2015
Admission routes1
Has abstractyes

Explore more

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