Evaluation of the effect of physical therapy on pain and dysfunction of knee osteoarthritis based on fNIRS: a randomized controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a chronic musculoskeletal disease that can cause joint pain and dysfunction, affecting the quality of life of patients. Nonsurgical treatment is the conventional treatment of KOA, among which physical therapy is widely used because of its simplicity, convenience and effectiveness. The functional biomarker will add to the clinical fidelity and diagnostic accuracy. Therefore, our study chose a more objective evaluation indicator, functional near-infrared spectroscopy (fNIRS), to identify between healthy people and KOA patients, and to detect the pain change before and after treatment of KOA patients. METHODS: The study will be conducted in the Rehabilitation Medical Center of West China Hospital of Sichuan University and divided into 2 stages. In the first stage, we will compare and determine the differences in baseline data between healthy volunteers and KOA patients. In the second stage, 72 KOA patients will be randomly divided into two groups: the drug therapy group (DT) and the combination therapy group (CT) for 10 treatments. Outcome measures will be measured at baseline and on the 5th and 10th days after the intervention, including the numerical rating scale (NRS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), pain catastrophizing scale (PCS), the association of pain severity with task-state functional connectivity fNIRS and association of pain severity with task-activated fNIRS. DISCUSSION: By analyzing the fNIRS data of healthy volunteers and KOA patients, our study will be determined whether fNIRS can be used as a new indicator to reflect the severity of pain in KOA patients. Subsequently, the same fNIRS data for KOA patients before and after the intervention will be collected to provide an accurate evaluation criterion for the effect of physical therapy on KOA. TRIAL REGISTRATION: The study was registered on the Chinese Registry website (registered in ChiCTR.org with the identifiers ChiCTR2200064175 and 29/09/2022).
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".