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Clinico-radiological correlation of osteoarthritis knee using Western Ontario and McMaster Universities score and Kellegren and Lawrance grading

2016· article· en· W2537080911 on OpenAlexaboutno aff
Manish Rajpoot, Digember Peepra, Krishna Kumar Pandey, H. K. Varma

Bibliographic record

VenueInternational Journal of Research in Medical Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal synovial abnormalities and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiological weaponGrading (engineering)OsteoarthritisPhysical therapyRadiologyPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Background: Diagnosis OA is basically based upon clinical and radiological features. In our study we have used a clinical scoring system of OA called as WOMAC (Western Ontario and McMaster Universities) score and a radiological staging system KL staging (Kellegren and Lawrance) OA radiological staging system and correlation between them.Methods: There were total 66 patients with 102 knees. X ray of bilateral knee was taken in weight bearing position (standing) and WOMAC score was calculated. X-rays were assessed with KL grading. WOMAC score a clinical scoring was correlated with a radiological KL grading for the OA of knee.Results: Range of WOMAC score was 11.5-67.7. Mean WOMAC score were 18.75, 31.31, 52.57 and 67.2 in patients of KL grade 1, 2, 3 and 4 respectively. Correlation between KL grading and WOMAC scoring were found to be significant; there were rise in the WOMAC scoring when KL grading increases.Conclusions: Both the KL grading and WOMAC score are directly proportional to each other, and hence, WOMAC scoring can be used to diagnose, assess the progression of the disease and the response to treatment of 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)0.002
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 score0.071
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.129
GPT teacher head0.425
Teacher spread0.297 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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