Prevalence and impact of constipation and bowel dysfunction induced by strong opioids: a cross-sectional survey of 520 patients with cancer pain: DYONISOS study
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of opioid-induced constipation (OIC) in patients with cancer pain according to the Knowles-Eccersley-Scott symptom score (KESS), the different symptoms of opioid-induced bowel dysfunction (OIBD), and to assess the impact of OIBD on patient's quality-of-life. METHODS: A cross-sectional observational study, using the KESS questionnaire and the physician's subjective assessment of constipation, and other questionnaires and questions on constipation, OIBD, and quality-of-life, carried out on 1 day at oncology day centres and hospitals. RESULTS: Five hundred and twenty patients were enrolled at 77 centres in France; 61.7% of patients (n = 321) showed a degree of constipation that is problematic for the patient according to KESS (between 9-39). Even more patients, 85.7% (n = 438), were considered constipated according to the physician's subjective assessment-despite laxative use (84.7% of patients). Quality-of-life was significantly reduced in constipated vs non-constipated patients for both PAC-QoL (p < 0.0001 for total score and each dimension) and the SF-12 questionnaires (statistically significant for all dimensions except physical state and role physical). OIC and OIBD led to hospitalization (16% of patients), pain (75% of patients), and frequent changes in opioid and laxative treatment. KEY LIMITATIONS: This cross-sectional study, in a selected population of cancer patients, has measured prevalence and impact of OIBD. Further confirmation could be sought through the use of longitudinal studies, and larger populations, such as non-cancer pain patients treated with opioids. CONCLUSIONS: Cancer patients taking opioids for pain are very frequently constipated, even if they are prescribed laxatives. This leads to relevant impairments of quality-of-life.
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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 | 0.001 | 0.001 |
| 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 it