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Severity grade of complaints from X-ray imaging on the elderlies with knee osteoarthritis

2023· article· en· W4389190188 on OpenAlexaboutno aff
Citra Puspa Juwita, Rita Damayanti, Besral Besral, Djohan Aras, James Wilson Hasoloan Manik

Bibliographic record

VenueInternational Journal of Community Medicine and Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersUniversitas Indonesia
KeywordsOsteoarthritisWOMACMedicinePhysical therapyElderly peopleInternal medicineGerontologyPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Background: This study aimed to investigate the correlation between severity of osteoarthritis with disorder or problem experienced by elder osteoarthritis patients. Methods: This was a cross-sectional study conducted in the period of October until December 2022 in one of the regions in the capital of Indonesia. Data was collected through knee X-ray examination according to Kellgren-Lawrence (KL) classification and direct interview on the elderlies with knee arthritis by using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. Data analysis was performed with Spearman rank. Results: From a total of 102 elderly participants who were diagnosed with knee osteoarthritis, it was found out that on average, as the osteoarthritis grade increased, the pain also increased. The stiffness and functional ability experienced by the participants were not according to the grade of the osteoarthritis. There was a weak correlation between severity with disorder or problem in the elderlies with osteoarthritis (p=0.030, R=0.214). Differences are only found in the severity of pain between grade 2 and grade 4, and in the severity of functional disorder between grade 1 and grade 3. Conclusions: Complaints of osteoarthritis in the elderlies are not specific for each severity grade.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.339
Teacher spread0.264 · 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 designOther design
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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