The Effect of Graded Motor Imagery on Pain and Function in Individuals with Knee Osteoarthritis
Bibliographic record
Abstract
This dataset was collected as part of the study titled "The Effect of Graded Motor Imagery on Pain and Function in Individuals with Knee Osteoarthritis" and includes data from patients with knee osteoarthritis (PwKOA) between July 2023 and March 2024. The data were collected using several clinical measurement tools, including the Visual Analog Scale (VAS), Mini-Mental State Examination (MMSE), Algometer, Digital Goniometer, Hand-Held Dynamometer, Timed Up and Go test (TUG), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC).The dataset contains variables such as gender, body mass index (BMI), age, disease duration, and other clinical outcome measures. The primary aim of the study was to examine whether graded motor imagery (GMI) was as effective as transcutaneous electrical nerve stimulation (TENS) in improving pain and functionality in patients with knee osteoarthritis.The data is provided in SPSS .sav format and can be used for secondary purposes, such as creating meta-analyses.The dataset is not publicly available but can be requested from the corresponding author for legitimate research purposes. Please contact ptsemraoguz@gmail.com for data access.This dataset is limited to individuals with knee osteoarthritis and should be interpreted within this context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".