Benzodiazepine Use and Morbidity-Mortality Outcomes in a Geriatric Palliative Care Unit: A Retrospective Review
Bibliographic record
Abstract
INTRODUCTION: Patients often experience delirium at the end of life. Benzodiazepine use may be associated with an increased risk of developing delirium. Alternate medications used in conjunction with benzodiazepines may serve as an independent precipitant of delirium. The aim is to understand the role of benzodiazepines in precipitating delirium and advanced mortality in palliative care population at the end of life. METHODS: A retrospective medical chart review was conducted at a hospice and palliative care inpatient unit between the periods of June 2017-December 2017 and October 2017-November 2018. It included patients in hospice and palliative care inpatient units who received a benzodiazepine and those who did not. Patient characteristics, as well as Palliative Performance Scale score, diagnosis, and occurrence of admission, terminal, and/or recurrent delirium, were collected and analyzed. RESULTS: Use of a benzodiazepine was not significantly associated with overall mortality nor cause-specific death without terminal delirium rate. However, it was significantly associated with higher cause-specific death with terminal delirium rate and a higher recurrent delirium rate. DISCUSSION: This retrospective chart review suggests an association between benzodiazepine use and specific states of delirium and cause-specific death. However, it does not provide strong evidence on the use of this drug, especially at the end of life, as it pertains to the overall mortality rate. Suggested is a contextual approach to the use of benzodiazepines and the need to consider Palliative Performance Scale score and goals of care in the administration of this drug at varying periods during patient length of stay.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".