The Assessment and Management of Delirium in Cancer Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it