Unsupervised subgrouping of chronic low back pain patients treated in a specialty clinic
Bibliographic record
Abstract
Abstract Background Chronic low back pain (cLBP) is the leading cause of disability worldwide. Current treatments have minor or moderate effects, partly because of the idiopathic nature of most cLBP cases, the complexity of its presentation, and heterogeneity in the population. Explaining this complexity and heterogeneity by identifying subgroups of patients is critical for personalized health. Clinical decisions tailoring treatment to patients’ subgroup characteristics and specific treatment responses can improve health outcomes. Current patient stratification tools divide cases into subgroups based on a small subset of characteristics, which may not capture many factors determining patient phenotypes. Methods and Findings In this study, we use an unsupervised machine learning framework to identify patient subgroups within a specialized back pain clinic and evaluate their outcomes. Our analysis identified 25 latent factors determining patient phenotypes and found three distinctive clusters of patients. The research suggests that there is heterogeneity in the population of patients treated in a specialty setting and that several factors determine patient phenotypes. Cluster 1 consists of those individuals with characteristics found to be protective of chronic pain: younger age, low pain medication prescription, high function, good insurance access, and low overlapping pain conditions. Individuals in Cluster 3 associate with older age and present with a higher incidence of chronic overlapping pain conditions, comorbidities, and pain medication use. Cluster 2 is an intermediate group. Conclusions We quantify cLBP population heterogeneity and demonstrate how ML analytical workflow can be used to explain, in part, this heterogeneity in relation to outcomes. Notably, considering a data-driven approach from multi-domain data produces different subgroups than the STarT back screening tool, and the addition of other functional metrics at baseline such as global physical and mental function, and pain intensity, increases the variance explained in outcomes. Our study provides novel insights into the complex nature of cLBP and the potential for data-driven methods to identify clinically relevant subtypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".