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Record W1998411139 · doi:10.1142/s0218957706001650

COMPARING QUALITY OF LIFE AMONG PEOPLE WITH DIFFERENT PATTERNS AND SEVERITIES OF KNEE OSTEOARTHRITIS

2006· article· en· W1998411139 on OpenAlexaboutno aff
Boonsin Tangtrakulwanich, Virasakdi Chongsuvivatwong, Alan Geater

Bibliographic record

VenueJournal of Musculoskeletal Research · 2006
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisMedicineQuality of life (healthcare)Physical therapyBody mass indexMarital statusPopulationInternal medicinePathologyAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

Objective: To identify what extent different patterns and severities of involvement affect quality of life of people suffering knee osteoarthritis. Methods: This population-based survey involved 288 women and 288 men aged 40 years or older from Songkhla province, southern Thailand. Quality of life was measured using the Medical Outcome Study Short Form Health sutvery (SF-36) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Radiographic investigation included antero-posterior and skyline view of both knees. Osteoarthritis was categorized into 3 patterns; isolated patellofemoral, isolated tibiofemoral and combined with diagnosis based on Kellgren & Lawrence grade 2 or higher. Results: Quality of life as measured by SF-36 and WOMAC showed poorer score in moderate or severe grade than in mild grade of severity. Isolated patellofemoral and combined patterns demonstrated showed poorer scores on both WOMAC and SF-36 than isolated tibiofemoral pattern. Body mass index, income level and pattern of involvement could independently predict total scores of WOMAC, while age, marital status and pattern of involvement affected total score of SF-36. Conclusion: Pattern of involvement is a better predictor of quality of life than disease severity in patients with knee osteoarthritis.

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)0.000
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 score0.010
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.329
Teacher spread0.291 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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