Transforming Chronic Pain Care Through Telemedicine: An Italian Perspective
Bibliographic record
Abstract
Chronic pain (CP) is a complex and debilitating condition that significantly impairs quality of life and imposes a high burden on healthcare systems. This study aims to evaluate the impact of telemedicine on chronic pain management in cancer survivors with complex CP. Our multicenter retrospective investigation of cancer survivors with complex CP included 100 patients (median age 65 years, 62% female). Pain, disability, and self-perceived health status were assessed using the Numeric Rating Scale (NRS), Brief Pain Inventory (BPI), Oswestry Disability Index (ODI), and the EuroQolfive-dimension five-level (EQ-5D-5L) questionnaire. The most common diagnoses were neuropathic pain (54%) and complex chronic pain (32%). Significant clinical improvements were observed after six months of telemedicine intervention (all p < 0.001). NRS scores improved by more than four points in 77% of patients, BPI Worst Pain Scores decreased by four points in 52% and by five points in 28% of patients. All patients’ disability levels improved from severe (median ODI score of 52) to moderate (median ODI score of 30). Self-perceived health status improved from 40 to 60 on the EQ-5D-5L scale. Telemedicine interventions significantly reduced pain intensity, decreased disability levels, and enhanced quality of life in chronic pain patients. These findings underscore the transformative potential of telemedicine in chronic pain management and support its broader integration into medical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".