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Record W4292959273 · doi:10.20492/aeahtd.1053581

Determination of Activities of Daily Living Problems in Patients with Lateral Epicondylitis and Investigation of the Relationship between Pain and Perceived Occupational Performance and Satisfaction

2022· article· en· W4292959273 on OpenAlexaboutno aff
Berkan Torpil, Özgür Kaya

Bibliographic record

VenueAnkara Eğitim ve Araştırma Hastanesi Tıp Dergisi · 2022
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyGynecologyMedicine

Abstract

fetched live from OpenAlex

Aim: Lateral epicondylitis is one of the most common diseases in the upper extremity that causes pain in the elbow, negatively affects activities of daily living. This study was planned to determine the activities of daily living in adults with lateral epicondylitis and to examine the relationship between pain and perceived occupational performance and satisfaction in activities of daily living.
 Materials and methods: A total of 56 individuals with a diagnosis of lateral epicondylitis, 17 males and 39 females, with a mean age of 52,18±5,92 years, completed the study. Visual Analogue Scale (VAS) was used to evaluate pain, and Canadian Occupational Performance Measure (COPM) was used to determine perceived occupational performance and satisfaction level.
 Results: According to the VAS, the mean pain at rest of the participants was 2,40±0,72, and the mean of pain during activity was 6,14±1,11. According to COPM, the perceived occupational performance mean of the participants in activities of daily living was 4,13±1,04 and the mean satisfaction was 4,07±1,17. When the correlation between pain and activities of daily living was examined, there was no correlation between pain at rest and perceived occupational performance (r=-0,015, p=0,911) and satisfaction (r=-0,064, p=0,639). A moderately strong negative was found between pain during activity and perceived occupational performance (r=-0,729, p

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.345

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)0.0000.000

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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