Frequent Pain Assessment May Interfere with Chronic PainRecovery: 5 Pilot Studies
Bibliographic record
Abstract
Background/Objectives: Catastrophization and other comorbid psychological factors that increase awareness of pain are known to hinder chronic pain recovery. Five pilot studies, reported here, indicate that increased pain awareness from frequent pain assessment may impede chronic pain recovery through similar mechanisms. Methods: All study participants were asked to use a biofeedback device at least twice daily. Studies 1 and 2 required pain assessment before and after each biofeedback session, while Studies 3-5 did not. Pain levels were measured by the McGill Pain Inventory (McGill PI) at the start and end of each study. Results: Studies 3-5 replicated our prior work, demonstrating changes in pain, anxiety, and satisfaction and recovery following a home-use biofeedback program. Participants in the studies without frequent pain assessment experienced greater overall reductions in pain compared to participants in studies that did not require pain assessment. The results are, however, statistically inconclusive due to high variability, so further research is necessary. Conclusion: The results of our study suggest that frequent pain assessment may hinder pain reduction, likely by facilitating negative thought patterns and neural circuits involved in chronic pain. These findings can be applied to developing better research designs and more effective chronic pain recovery programs.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".