Pain Assessment and Management in Oncological Practice: A Survey from the Italian Network of Supportive Care in Oncology
Bibliographic record
Abstract
Background/Objectives: Cancer pain is prevalent across all stages of the disease, significantly impacting patients’ lives. Despite the availability of guidelines, its assessment and management remain suboptimal in many clinical settings. This study aimed to explore how healthcare professionals in Italy assess and manage cancer pain, identifying gaps and educational needs to improve adherence to best practices. Methods: A multidisciplinary Scientific Board designed an online survey comprising 28 items addressing demographics, pain assessment tools, perception of pain, pharmacological management, adverse effects, and barriers to care. The survey targeted oncologists, nurses, radiotherapists, and surgeons within the Italian Network of Supportive Care in Oncology. Data were collected from March to May 2024 and analyzed descriptively. Results: Eighty-five professionals participated, predominantly oncologists (63.5%). Most respondents utilized pain scales, with the Numerical Rating Scale (60.3%) being the most frequent. However, specific tools like the Edmonton Symptom Assessment System (ESAS) were underutilized, possibly due to limited training and time constraints. Factors influencing analgesic choice included patient comorbidities (30.3%) and polypharmacy (28.0%). The main barriers to effective pain management included inadequate training (85.5%) and poor communication between patients and caregivers (40.6%) and within care teams (31.9%). Preventive measures for opioid-induced adverse events were widely employed, with laxatives (52.7%) and antiemetics (40.5%) being the most common. Conclusions: Findings underscore the need for structured training programs, improved communication, and integration of validated assessment tools. A multidisciplinary, proactive approach to cancer pain assessment and management is essential to optimize care and reduce its burden across all disease stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".