Quantitative Sensory Testing Protocols to Evaluate Central and Peripheral Sensitization in Knee OA: A Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Quantitative sensory testing (QST) methods have become widely used for the assessment of nervous system sensitization to nociceptive signalling in studies of people with knee osteoarthritis (OA). However, few standardised QST protocols have been developed. Variability in their execution may lead to differences in their interpretation. OBJECTIVE: The proposed scoping review will seek to identify various QST methodologies being used in the assessment of sensitization and how sensitization is being defined in people with knee OA. Methods and Analysis: This scoping review will be guided by existing scoping review methodologies. Relevant studies will be extracted from the following electronic databases: Medical Literature Analysis and Retrieval System Online, ExcerptaMedica Database, Allied and Complementary Medicine Database and the Cumulative Index to Nursing Allied Health Literature. Independent screening of the abstracts and full articles and data extraction will be performed in pairs. Information extracted will focus on qualitative and quantitative data relevant to the content of the protocols from included studies. Data will be summarised in order to draw conclusions on the common elements used in QST protocols and definitions of sensitization for knee OA. CONCLUSION: This scoping review will provide insight into the most common methods of QST used in the assessment of nociceptive signaling in people with knee OA. This will potentially identify areas where a systematic review or other primary research may be required in order to develop fixed evidence-based protocols for QST in patients with knee OA.
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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 | 0.162 | 0.190 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".