Delirium in cancer patients: a focus on treatment-induced psychopathology
Bibliographic record
Abstract
PURPOSE OF REVIEW: Delirium is a neuropsychiatric syndrome that occurs frequently in cancer patients, especially in those with advanced disease. Recognition and effective management of delirium is particularly important in supportive and palliative care, especially in view of the projected increase in the elderly population and the consequent potential for the number of patients both diagnosed and living longer with cancer to increase substantively. RECENT FINDINGS: Studies of delirium in a variety of settings have generated new insights into phenomenology, assessment tools, the psychomotor subtypes, potential patho-physiological markers, pathogenesis, reversibility, and the role of sedation in symptom control. SUMMARY: Validated tools exist to assist in the assessment of delirium. Although our understanding of the pathogenesis of delirium has improved somewhat, there remains a compelling need to further elucidate the underlying pathophysiology, especially in relation to opioids and the other psychoactive medications that are used in supportive care. Further trials are needed, especially in patients with advanced disease to determine predictive models of reversibility, preventive strategies, outcomes, and to assess the role of antipsychotic and other medications in symptomatic management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".