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Record W2783531443 · doi:10.1177/2325967117750082

Comparison of 3 Knee-Specific Quality-of-Life Instruments for Patients With Meniscal Tears

2018· article· en· W2783531443 on OpenAlexaboutno aff
Mirco Sgroi, Semra Kocak, Heiko Reichel, Thomas Kappe

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACOsteoarthritisQuality of life (healthcare)Physical therapyTearsMinimal clinically important differenceCohortKnee painSurgeryInternal medicineRandomized controlled trialPathology

Abstract

fetched live from OpenAlex

Background: Meniscal tears are a common cause of knee pain and disability. The objective measurement of the health-related quality of life of patients with meniscal tears plays a key role in clinical evaluation and therapeutic decision making. Several evaluation tools have been used to measure the effects of meniscal tears on knee function and quality of life. However, most of these tools are nonspecific for meniscal pathology. Purpose/Hypothesis: The purpose of the present study was to compare the capability of 3 commonly used knee assessment tools to measure the impact of meniscal tears on knee function and quality of life: the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Knee injury and Osteoarthritis Outcome Score (KOOS), and the Western Ontario Meniscal Evaluation Tool (WOMET). Our null hypothesis was that no difference would exist among the 3 assessment tools. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 207 consecutive patients (mean ± SD: age, 52.6 ± 14.3 years) with arthroscopically confirmed meniscal tear were included. Preoperatively, 3 knee function and quality-of-life scores were obtained: KOOS, WOMAC, and WOMET. The relative outcome scores of the questionnaires were compared postoperatively. Results: The sum scores (relative scores) were as follows: 234.2 ± 92.5 (55.7%) for the KOOS, 132.6 ± 54.3 (55.5%) for the WOMAC, and 113 ± 30.8 (71%) for the WOMET. The relative score results for the WOMET were significantly higher than those for the WOMAC and the KOOS (both P < .01), while no significant difference was found between the WOMAC and the KOOS ( P = .735). Conclusion: A greater impact on health-related quality of life for patients with meniscal tears can be measured with the WOMET when compared with the WOMAC and the KOOS. Therefore, using the WOMET can be recommended for the evaluation of knee function and quality-of-life impairment of patients with meniscal tears.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.339
Teacher spread0.311 · 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.

Study designObservational
DomainMethods
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

Citations7
Published2018
Admission routes1
Has abstractyes

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