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Record W2162314860 · doi:10.1136/bjsm.2002.002568

Current practice in the management of anterior cruciate ligament injuries in the United Kingdom: Figure 1

2004· article· en· W2162314860 on OpenAlexaff
Birender Kapoor, Darren Clement, Alexandra Kirkley, Nicola Maffulli

Bibliographic record

VenueBritish Journal of Sports Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineAnterior cruciate ligamentAnterior Cruciate Ligament InjuriesOrthopedic surgeryPhysical therapySurgeryGeneral surgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To outline the current practice in the management of anterior cruciate ligament (ACL) injuries in the United Kingdom. METHODS: A postal questionnaire designed to include various clinical scenarios was sent out to the 321 orthopaedic surgeons in the United Kingdom who, being affiliated to one of the specialist societies of the British Orthopaedic Association, namely the British Association for Surgery of the Knee (BASK) or the British Orthopaedic Sports Trauma Association (BOSTA), have a manifested interest in treating such injuries. RESULTS: The response rate was 60% (192/321). Most surgeons diagnose and operate on less than 50 ACL injuries a year. The following results were obtained: 58% (76/132) use bone-patellar tendon-bone autografts, whereas 33% (44/132) use semitendinosis/gracilis autografts; 84% (108/129) would not incorporate the ACL remnant in the reconstruction; 14% (19/135) would perform an ACL reconstruction in an 8 year child with an acute rupture; 30% (42/141) would perform an ACL reconstruction in a 14 year old with an acute ACL rupture. CONCLUSIONS: There is wide variation in the management of acute and chronic ACL injuries among orthopaedic surgeons in the British Isles. Future research and randomised controlled trials should address the issues that this investigation has raised.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations73
Published2004
Admission routes1
Has abstractyes

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