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Record W4293531170 · doi:10.3390/healthcare10091631

A Systematic Review of Psychometric Properties of Knee-Related Outcome Measures Translated, Cross-Culturally Adapted, and Validated in Arabic Language

2022· review· en· W4293531170 on OpenAlexaboutno aff
Mahamed Ateef, Mazen Alqahtani, Msaad Alzhrani, Abdulaziz A. Alkathiry, Ahmad Alanazi, Shady Alshewaier

Bibliographic record

VenueHealthcare · 2022
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsArabicOutcome (game theory)Natural language processingPsychologyComputer scienceLinguisticsMathematicsPhilosophy

Abstract

fetched live from OpenAlex

During the previous two decades, patient-reported outcome measures (PROMs) have been well tested, and the tools were validated in different languages across the globe. This systematic review aimed to identify the knee disease-specific outcome tools in Arabic and evaluate their methodological quality of psychometric properties of the most promising tools based on the COSMIN checklist and PRISMA guidelines. Articles published in English, from the inception of databases until the date of search (10 August 2022), were included. Articles without at least one psychometric property (reliability, validity, and responsiveness) evaluation, and articles other than in the English language, were excluded from the study. The key terms ["Arabic" AND "Knee" AND ("Questionnaire" OR "Scale")] were used in three databases, i.e., PubMed, Scopus, and Web of Science (WoS) in the advanced search strategy. Key terms were either in the title or abstract for PubMed. Key words were in the topic (TS) for WoS. COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) risk of bias checklist was used to evaluate the methodological quality of psychometric properties of the Arabic knee-related outcome measures. A total of 99 articles were identified in PubMed, SCOPUS, and WoS. After passing inclusion and exclusion criteria, 20 articles describing 22 scales from five countries were included in this review. The instruments validated in the Arabic language are Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), knee injury and osteoarthritis outcome score (KOOS), knee outcome survey- activities of daily living scale (KOS-ADLS), Oxford knee score (OKS), anterior knee pain scale, osteoarthritis of knee and hip health-related quality of life (OAKHQoL) scale, Lysholm knee score (LKS), international documentation committee subjective knee form (IKDC), intermittent and constant osteoarthritis pain (ICOAP) questionnaire, Kujala patellofemoral pain scoring system (PFPSS), anterior knee pain scale (AKPS) and osteoarthritis quality of life questionnaire (OAQoL),. All were found to have good test-retest reliability (Intra Correlation Coefficient), internal consistency (Cronbach's alpha), and construct validity (Visual Analog Scale, Short Form-12, RAND-36, etc.). Of 20 instruments available to assess self-reported knee symptoms and function, 12 were validated in the Saudi Arabian population. Among them, KOS-ADLS is the best PROM to be used in various knee conditions, followed by KOOS and WOMAC. The assessed methodological quality of evidence says that the knee Arabic PROMs are reliable instruments to evaluate knee symptoms/function.

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)0.024
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.111
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0140.017
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.109
GPT teacher head0.384
Teacher spread0.275 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2022
Admission routes1
Has abstractyes

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