154
Bibliographic record
Abstract
Introduction: Rapid response teams (RRT) are called for a variety of causes in our tertiary care oncologic institution. We perceive that a significant number of calls revolve around end of life (EOL). To our knowledge, RRT interactions at EOL have not been studied in an oncologic population. Hypothesis: The purpose of this study was to characterize EOL interactions and mortality around RRT calls. Methods: Using ICU and hospital databases we identified all RRT calls from 1/1/12 to 5/30/12. We analyzed DNR status (prior and after RRT) and mortality (independent of DNR status at time of RRT). Results: During the study period, 346 RRT calls were activated on 329 inpatients (12 calls were multiple, 3 calls were triple). DNR orders were a significant component of the RRT calls in 21.9% (n=72) of patients. 7.6 % (n=25) had DNR orders prior to RRT activation. However, another 14.3% (n=47) were made DNR within 24 hours of the RRT call in collaboration with the RRT providers. 10.3% (n=34) of the RRT calls were dead within 24 hours, 12.5% (n=41) within 48 hours and 23% (75) were dead within 1 week of the RRT call. Conclusions: Nearly a quarter of RRT calls at our tertiary cancer center involve EOL discussions. Additionally, almost a quarter of the patients died within a week of the RRT call. These findings suggest that EOL training and counseling should be an integral part of RRT provider education and practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.519 | 0.413 |
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".