Bibliographic record
Abstract
Introduction The supportive care of cancer patients routinely involves management of multiple symptoms as well as neuropsychiatric disorders associated with cognitive dysfunction, notably delirium, but also in some instances depression. Cancer therapies themselves (especially medications) can cause or exacerbate cognitive dysfunction. Cognitive disorders of all types are the second most common psychiatric disorders experienced by cancer patients after mood disorders (Derogatis et al ., 1983). Patients in particular settings and stages of the disease continuum are at particular risk for cognitive impairment, with potential implications for prognosis. The boundaries between these disorders are not always distinct, which complicates accurate diagnosis and treatment. Co-morbidity is common. The stigma associated with mental illness and the physical burdens of caring for affected patients place family members and other caregivers at increased risk for physical and emotional distress. The common cognitive disorders seen in the oncology setting often respond well to treatment. In other cases, palliation of symptoms is possible and individual patients may respond to creative and unconventional medication interventions. Here we discuss common neuropsychiatric syndromes and clinical settings associated with cognitive dysfunction and altered mental status and behavior, interventions, and potential areas for future research. Delirium The American Psychiatric Association defines delirium as a syndrome characterized by rapid onset of impaired cognition, and altered consciousness, and it is presumed to be due to one or more physical or disease-related factors (Table 18.1, American Psychiatric Association, 2000; DSM-IV TR).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".