How to objectively assess and observe maladaptive pain behaviors in clinical rehabilitation: a systematic search and review
Bibliographic record
Abstract
BACKGROUND: Cognitive-affective factors influence the perception of pain and disability. These factors can lead to pain behaviors (PB) that can persist and become maladaptive. These maladaptive PB will further increase the risk of chronicity or persistence of symptoms and disability. Thus, clinicians must be prepared to recognize maladaptive PB in a clinical context. To date, in the context of assessment in a rehabilitation setting, PB in clinical settings are poorly documented. The main objective of this study was to identify direct observation methods and critically appraise them in order to propose recommendations for practice. As a secondary objective, we explored and extracted the different observable PB that patients could exhibit and that clinicians could observe. METHODS: We conducted a comprehensive review on four databases with a generic search strategy in order to obtain the largest range of PB. For the first objective, a two-step critical appraisal used clinical criteria (from qualitative studies on barriers to implement routine measures) and psychometric criteria (from Brink and Louw critical appraisal tool) to determine which observation methods could be recommended for clinical practice. For the second objective, we extracted PB found in the literature to list potential PB that patients could exhibit, and clinicians could observe. RESULTS: From the 3362 retrieved studies, 47 met the inclusion criteria for the first objective. The clinical criteria allowed us to select three observation methods. After the psychometric step, two observation methods were retained and recommended for clinical practice: the Behavioral Avoidance Test-Back Pain (BAT-Back) and the Pain Behaviour Scale (PaBS). For the second objective, 107 studies met the inclusion criteria. The extraction of the PB allowed us to list a large range of PB and classify the data in 7 categories of PB. CONCLUSION: Our results allowed us to recommend two observation methods for clinical practice. However, these methods have limitations and are validated only in chronic low back pain populations. With the extraction of PB presented in the literature, we contribute to better prepare clinicians to recognize PB in all patients who are experiencing pain.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".