Adequacy of cancer-related pain management and predictors of undertreatment at referral to a pain clinic
Bibliographic record
Abstract
Background: Several guidelines have advocated the need for adequate cancer-related pain (CRP) management. The pain management index (PMI) has been proposed as an auditable measure of the appropriateness for analgesic therapy. Objectives: To determine the adequacy of CRP management based on the PMI status and its patient-related predictors at the point of referral to a pain clinic (PC). Methods: Consecutive patients referred to a PC had standardized initial assessments and status documentation on the Brief Pain Inventory (BPI) ratings; pain mechanism, using a neuropathic pain diagnostic questionnaire (the Douleur Neuropathique 4 tool); episodic pain; oral morphine equivalent daily dose; the Hospital Anxiety Depression Scale and the Emotion Thermometer scores; and cancer diagnosis, metastases, treatment, and pain duration. Predictors of “negative PMI status” [PMI(−)] were examined in logistic regression models. Variables with p <0.25 in an initial bivariable analysis were entered into a multivariable model. Results: Of 371 participants, 95 (25.6%) had PMI(−), suggesting undertreatment of CRP. Both female sex and higher scores on the BPI’s “interference with general activity” more strongly predicted PMI(−). Patients who received either radiotherapy or one or more adjuvant analgesics prior to the initial consultation at the PC, those who had neuropathic pain, those who had a greater need for emotional help, and those with higher BPI’s “relief” scores were all less likely to be PMI(−). Conclusion: The potential burden of patient and family distress associated with suboptimal CRP management in one in four patients should generate major public health concern and prompt appropriate educational and health policy measures to address the deficit. Keywords: cancer pain, pain management, opioid analgesics, pain measurement
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".