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Record W2467818953 · doi:10.1111/sms.12707

Comparison of patient‐reported outcomes among those who chose <scp>ACL</scp> reconstruction or non‐surgical treatment

2016· article· en· W2467818953 on OpenAlexfundno aff
Clare L. Ardern, Sofi Sonesson, Magnus Forssblad, Joanna Kvist

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2016
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsChoseMedicineACL injuryOsteoarthritisQuality of life (healthcare)Anterior cruciate ligamentSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

The aim of our study was to cross-sectionally compare patient-reported knee function outcomes between people who chose non-surgical treatment for ACL injury and those who chose ACL reconstruction. We extracted Knee Injury and Osteoarthritis Outcome Score (KOOS) and EuroQoL-5D data entered into the Swedish National ACL Registry by patients with a non-surgically treated ACL injury within 180 days of injury (n = 306), 1 (n = 350), 2 (n = 358), and 5 years (n = 114) after injury. These data were compared cross-sectionally to data collected pre-operatively (n = 306) and at 1 (n = 350), 2 (n = 358), and 5 years (n = 114) post-operatively from age- and gender-matched groups of patients with primary ACL reconstruction. At the 1 and 2 year comparisons, patients who chose surgical treatment reported superior quality of life and function in sports (1 year mean difference 12.4 and 13.2 points, respectively; 2 year mean difference 4.5 and 6.9 points, respectively) compared to those who chose non-surgical treatment. Patients who chose ACL reconstruction reported superior outcomes for knee symptoms and function, and in knee-specific and health-related quality of life, compared to patients who chose non-surgical treatment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.350
Teacher spread0.321 · 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 designObservational
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

Citations59
Published2016
Admission routes1
Has abstractyes

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