The Assessment and Management of Delirium in Cancer Patients
Bibliographic record
Abstract
Abstract Learning Objectives After completing this course, the reader will be able to: Summarize the current evidence regarding strategies for the assessment and management of delirium in advanced cancer.Outline the medications most commonly implicated for drug-induced delirium.Compare the various pharmacological agents available for use in managing cancer-related delirium. This article is available for continuing medical education credit at CME.TheOncologist.com Delirium remains the most common and distressing neuropsychiatric complication in patients with advanced cancer. Delirium causes significant distress to patients and their families, and continues to be underdiagnosed and undertreated. The most frequent, consistent, and, at the same time, reversible etiology is drug-induced delirium resulting from opioids and other psychoactive medications. The objective of this narrative review is to outline the causes of delirium in advanced cancer, especially drug-induced delirium, and the diagnosis and management of opioid-induced neurotoxicity. The early symptoms and signs of delirium and the use of delirium-specific assessment tools for routine delirium screening and monitoring in clinical practice are summarized. Finally, management options are reviewed, including pharmacological symptomatic management and also the provision of counseling support to both patients and their families to minimize distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".