Pre-admission escalation rate of daily opioid consumption (PERDOC), and total morphine equivalent daily dose on the first complete day of admission (D1-MEDD) to a tertiary-level palliative care unit (TPCU): Correlates and predictors in patients with advan
Bibliographic record
Abstract
9065 Background: Although animal laboratory studies attest to opioid tolerance (OT), it can be difficult in clinical practice to determine whether substantive escalation in opioid dose (average of >5% per day between initial daily dose and maximum daily dose to date) is due to disease progression (DP) or decreased opioid responsiveness, which in turn may be due to OT or other factors. Our study aims were to determine (1) the frequency of PERDOC>5%, (2) the correlates and predictors of PERDOC>5% and D1-MEDD. Methods: We retrospectively examined TPCU patient database records for demographics, physician rating of PERDOC in the Edmonton Staging System (ESS) for cancer pain classification, Edmonton Symptom Assessment System (ESAS) scores, and D1-MEDD. Consecutive 1st admission data on patients surviving >3 days were included in the initial analysis. Using complete data, logistic regression and multiple regression models were created with PERDOC>5% and logn D1-MEDD, respectively, as dependent variables. Results: From 1,351 patients who met the initial descriptive analysis eligibility criteria, 1212 (90%) had an ESS rating for PERDOC. The prevalence of PERDOC>5% was 274/1212 (19.3%). Bivariate analysis (N=969, complete data) showed that PERDOC>5% was positively associated (p<0.05) with younger age, neuropathic pain component (NPC), a pathological level of psychological distress (PLPD), substance abuse, higher D1-MEDD, and higher ESAS pain score (ESAS-P). In the multivariate analysis, NPC (Odds ratio: 2.1, 95% confidence interval: 1.5–2.9), PLPD (1.7, 1.2–2.6), and higher D1-MEDD (2.2, 1.03–4.7) were the strongest independent positive predictors, and ESAS-P (1.01, 1.003–1.02) remained as a weaker predictor. In the D1-MEDD regression model, positive predictors (p<0.05) were younger age, NPC, incident pain, PERDOC>5%, PLPD, ESAS-P and ESAS anxiety scores. Conclusions: Aside from OT and DP, the multiple predictors identified for PERDOC>5% and D1-MEDD underscore the need for a systematic multidimensional assessment of cancer pain that incorporates psychological and physical characteristics. No significant financial relationships to disclose.
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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 | 0.000 | 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 | 0.000 |
| 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".