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Record W4288067904 · doi:10.7759/cureus.27374

Occupational Therapy Using Coping Lists After Total Knee Arthroplasty: A Case Series

2022· article· en· W4288067904 on OpenAlexaboutno aff
Ryusei Hara, Yuki Hiraga, Yoshiyuki Hirakawa

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoping (psychology)Physical therapyHospital Anxiety and Depression ScaleVisual analogue scaleAnxietyArthroplastyOccupational therapyTotal knee arthroplastyRating scaleOsteoarthritisClinical psychologySurgeryPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Total knee arthroplasty (TKA) can improve the postoperative quality of life in patients with severe knee osteoarthritis. Although occupational therapy (OT) using a coping list may be useful for post-TKA patients, its use has not been documented. This study aimed to explore the effectiveness of OT using coping skills. Five post-TKA patients underwent OT using coping skills. The Canadian Occupational Performance Measure (COPM), numerical rating scale (NRS), Hospital Anxiety and Depression Scale (HADS), EQ-5D (EuroQol-5-dimension)-5-level (5L), EQ-5D Visual Analogue Scale (VAS), modified fall efficacy scale (MFES), Pain Disability Assessment Scale (PDAS), and coping skills were measured at the start and end of the study. Significant improvements were observed in COPM, NRS, HADS, EQ-5D-5L, and PDAS scores (p <0.05). No significant improvements were found in the EQ-5D VAS and MFES scores. All evaluations showed a large effect size (r ≤ 0.5). The total number of coping skills also increased. This report suggests that OT with coping strategies is effective for pain, psychological factors, quality of life, and activities of daily living. Incorporating coping skills in OT may be useful in postoperative TKA pain management. However, larger studies are needed to validate this.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.036
GPT teacher head0.304
Teacher spread0.269 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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