Exploring pain phenotypes in workers with chronic low back pain: Application of IMMPACT recommendations
Bibliographic record
Abstract
Background: Chronic low back pain (CLBP) is a major cause of disability globally. Stratified care has been proposed as a means to improve prognosis and treatment but is generally based on limited aspects of pain, including biopsychosocial drivers.Aims: Following Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations, the present study explored pain phenotypes with a sample of workers with CLBP, a population for which no pain phenotypes have been derived to date.Methods: A cross-sectional design was used with a sample of 154 workers with CLBP attending a rehabilitation clinic, recruited in person and from social media. Latent class analysis was used to identify subgroups of patients with different pain profiles based on ten pain indicators (pain variability, pain intensity, pain quality, somatization, sleep quality, depression, fatigue, pain catastrophizing, neuropathic pain, and central sensitization).Results: The majority of the sample (85%) were recruited through social media. Both the two-class and three-class solutions were found to be satisfactory in distinguishing phenotypes of workers with CLBP. Three variables proved particularly important in distinguishing between the pain phenotypes—pain quality, fatigue, and central sensitization—with higher scores on these indicators associated with pain phenotypes with higher pain burden. Increased chronic pain self-efficacy, work-related support, and perceived work abilities were protective risk factors for being in a higher pain burden class.Conclusions: The present study is the first to explore IMMPACT recommendations for pain phenotyping with workers with CLBP. Future prospective research will be needed to validate the proposed pain phenotypes.
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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 | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".