Subgrouping of Low Back Pain Patients for Targeting Treatments
Bibliographic record
Abstract
INTRODUCTION: Many patients with low back pain (LBP) are treated in a similar manner as if they were a homogenous group. However, scientific evidence is available that pain is a complex perceptual experience influenced by a wide range of genetic, psychological, and activity-related factors. The leading question for clinical practice should be what works for whom. OBJECTIVES: The main aim of the present review is to discuss the current state of evidence of subgrouping based on genetic, psychosocial, and activity-related factors in order to understand their contribution to individual differences. RESULTS: Based on these perspectives, it is important to identify patients based on their specific characteristics. For genetics, very promising results are available from other chronic musculoskeletal pain conditions. However, more research is warranted in LBP. With regard to subgroups based on psychosocial factors, the results underpin the importance of matching patients' characteristics to treatment. Combining this psychosocial profile with the activity-related behavioral style may be of added value in tailoring the patient's treatment to his/her specific needs. CONCLUSIONS: For future research and treatment it might be challenging to develop theoretical frameworks combining different subgrouping classifications. On the basis of this framework, tailoring treatments more specifically to the patient needs may result in improvements in treatment programs for patients with LBP.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".