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Record W2047374052 · doi:10.1007/s11999-008-0468-0

Measuring Tools for Functional Outcomes in Total Knee Arthroplasty

2008· article· en· W2047374052 on OpenAlexaff
Robert B. Bourne

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2008
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineTotal knee arthroplastyWOMACPsychological interventionArthroplastyPhysical therapySports medicineQuality of life (healthcare)Orthopedic surgeryPatient satisfactionIntervention (counseling)Health careOsteoarthritisSurgeryAlternative medicineNursing

Abstract

fetched live from OpenAlex

Total knee arthroplasty has come under increasing scrutiny attributable to the fact that it is a high-volume, high-cost medical intervention in an era of increasingly scarce medical resources. Health-related quality-of-life outcomes have been developed such that healthcare providers might determine how good an intervention is and whether it is cost-effective. Total knee arthroplasty has been subjected to disease-specific, patient-specific, global health, functional capacity, and cost-to-utility outcome measures. Patient satisfaction is high (90%) after total knee arthroplasty and 93% of patients would have this operative procedure again. Large improvements in preoperative to postoperative WOMAC scores occurred (over 39 of 100 points in 82% of patients). Cost-to-quality outcomes demonstrated total knee arthroplasties are extremely cost-effective. This analysis documents total knee arthroplasty is a highly efficacious procedure that competes favorably with all medical and surgical interventions.

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)0.016
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.062
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.224
GPT teacher head0.402
Teacher spread0.177 · 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 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

Citations86
Published2008
Admission routes1
Has abstractyes

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