Maddalena Opioid Switching Score in patients with cancer pain
Bibliographic record
Abstract
ABSTRACT: Evaluation of opioid switching (OS) for cancer pain has not been properly assessed. The aim of this study was to assess an integrated score (Maddalena Opioid Switching Score) as a simple and repeatable tool to evaluate the outcomes of OS, facilitating the interpretation and comparison of studies, and information exchange among researchers. The integrated score took into account pain intensity, intensity of opioid-related symptoms, and cognitive function by using an author's formula. Physical and psychological symptoms were evaluated by the Edmonton Symptom Assessment Scale and Patient Global Impression (PGI) by the minimal clinically important difference. One hundred six patients were analyzed. Ninety-five patients were switched successfully, and 11 patients underwent a further OS and/or an alternative procedure. The Maddalena Opioid Switching Score significantly decreased after OS and was highly correlated to PGI of improvement ( P < 0.0005). In patients with unsuccessful OS, no significant changes in the Maddalena Opioid Switching Score and PGI were observed. A significant reduction in Edmonton Symptom Assessment Scale items intensity was observed after OS. The Maddalena Opioid Switching Score resulted to be a sensitive instrument for measuring the clinical improvement produced by OS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".