Modeling the Predictive Value of Pain Intensity on Costs and Resources Utilization in Patients With Peripheral Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVE: The aim of the present analysis was to model the association and predictive value of pain intensity on cost and resource utilization in patients with chronic peripheral neuropathic pain (PNP) treated in routine clinical practice settings in Spain. METHODS: We performed a secondary economic analysis based on data from a multicenter, observational, and prospective cost-of-illness study in patients with chronic PNP that is refractory to prior treatment. Pain intensity was measured using the Short-Form McGill Pain Questionnaire. Univariate and multivariate linear regression models were fitted to identify independent predictors of cost and health care/non-health care resource utilization. RESULTS: A total of 1703 patients were included in the current analysis. Pain intensity was an independent predictor of total costs ([total costs]=35.6 [pain intensity]+214.5; coefficient of determination [R(2)]=0.19, P<0.001), direct costs ([direct costs]=10.8 [pain intensity]+257.7; R=0.06, P<0.001), and indirect costs ([indirect costs]=24.8 [pain intensity]-43.4; R(2)=0.20, P<0.001) related to chronic PNP in the univariate analysis. Pain intensity remains significantly associated with total costs, direct costs, and indirect costs after adjustment by other covariates in the multivariate analysis (P<0.001). None of the other variables considered in the multivariate analysis were predictors of resource utilization. DISCUSSION: Pain intensity predicts the health care and non-health care resource utilization, and costs related to chronic PNP. Management of patients with drugs associated with a higher reduction of pain intensity may have a greater impact on the economic burden of that condition.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".