Barriers and Adherence to Pain Management in Advanced Cancer Patients
Bibliographic record
Abstract
AIM: To assess patients' barriers to pain management and analgesic medication adherence in patients with advanced cancer. METHODS: This was a prospective cross-sectional study in patients with advanced cancer receiving chronic opioid therapy. Age, gender, cancer diagnosis, Karnofsky level, and educational status were recorded. The Brief Pain Inventory (BPI), Edmonton Symptom Assessment Scale (ESAS), Memorial Delirium Assessment Scale (MDAS), Barriers Questionnaire II (BQ-II), Medication Adherence Rating Scale (MARS), and Hospital Anxiety and Depression Scale (HADS) were the measurement instruments used. RESULTS: One-hundred-thirteen patients were analyzed. The mean age was 68 (±13) years, and 59 (52%) were male. The mean Karnofsky status was 51.4 (standard deviation [SD] 11.5). The mean score for BQ-II items was 1.77 (SD 0.7). The BQ-II score was independently related to the HADS-Depression score (P = 0.033) and the total HADS score (P = 0.049). Negative side-effects and attitudes toward psychotropic medication globally prevailed among MARS items. These items were independently associated with gender (P = 0.030), pain (P = 0.003), and depression (P = 0.047). CONCLUSION: Barriers to pain management were mild. Psychological factors such as depression were the main factor associated with barriers. Poor adherence to analgesic medication was mostly manifested as negative side-effects and attitudes toward psychotropic medication, was more frequent observed in females, and was associated with the ESAS items pain and depression.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".