Frequency, Outcomes, and Associated Factors for Opioid-Induced Neurotoxicity in Patients with Advanced Cancer Receiving Opioids in Inpatient Palliative Care
Bibliographic record
Abstract
Context: Opioid-induced neurotoxicity (OIN) is an underdiagnosed yet distressing symptom in palliative care patients receiving opioids. However, there have been only a limited number of studies on OIN. Objectives: Our aim was to determine the frequency of and risk factors for OIN in patients receiving opioids during inpatient palliative care. Methods: We randomly selected 390 of 3014 eligible patients who had undergone palliative care consultations from January 2014 to December 2014. Delirium, drowsiness, hallucinations, myoclonus, seizures, and hyperalgesia were defined as OIN and were recorded. The other 10 common symptoms in cancer patients were assessed using the Edmonton Symptom Assessment Scale (ESAS). Patient demographics, morphine equivalent daily dose (MEDD), comorbidities, OIN management, and overall survival (OS) duration were also assessed. The associations between the incidence of OIN and MEDD, the other 10 symptoms, and OS were analyzed. Results: Fifty-seven (15%) patients had OIN. The most common symptom was delirium (n = 27). On multivariate analysis, a high MEDD (p = 0.020), high ESAS pain score (p = 0.043), drowsiness (p = 0.007), and a poor appetite (p = 0.014) were significantly associated with OIN. OIN was not significantly associated with a shorter OS duration (p = 0.80). Conclusions: OIN was seen in 15% of patients receiving opioids as part of inpatient palliative care. Although OIN was not associated with OS, routine monitoring is especially needed in cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".