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Record W7144846159

Estimation of difficulty in physical functioning observed in elderly women with knee pain by measuring the intercondylar distance of the femurs

2008· article· ja· W7144846159 on OpenAlexaboutno aff
Isamu Konishi

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2008
Typearticle
Languageja
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsKnee painOsteoarthritisElderly peopleCutoffFemurKnee Joint
DOInot available

Abstract

fetched live from OpenAlex

Background and purpose Knee pain is prevalent in elderly women and it increases with age. Simple screening methods to identify individuals with certain disabilities among elderly women with knee pain will be useful in community health activities. The purpose of this study was to determine a cutoff value of the intercondylar distance of the femurs to estimate the presence of some difficulty in physical functioning observed in elderly women with knee pain. Methods Twenty-one elderly women (age 70.0 ± 6.1 years [mean ± SD]) with knee pain were our study subjects. The degree of difficulty in physical functioning was assessed by the Western Ontario McMaster Universities Osteoarthritis Index (WOMAC). The functioning score, WOMAC-F, consisted of 17 items (possible range, 0-68). Intercondylar distance of the femurs was defined as the maximal diameter, in centimeters, of a wooden stick (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0 cm) or 2 or more sticks combined that could pass through the gap between the femurs without getting caught. The sensitivity and specificity of each distance was judged by considering WOMAC-F 4 as positive and WOMAC-F 0-3 as negative. Findings The most suitable cutoff points were 2.5 cm and 3.0 cm. Conclusions Evaluation of the intercondylar distance by using the reference value determined in the present study will be useful in community health activities for screening elderly women with knee pain.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.223
Teacher spread0.205 · 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 designBench or experimental
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

Citations0
Published2008
Admission routes1
Has abstractyes

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