The role of the medical emergency team in end-of-life care
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of medical emergency teams in end-of-life care planning. DESIGN: One month prospective audit of medical emergency team calls. SETTING: Seven university-affiliated hospitals in Australia, Canada, and Sweden. PATIENTS: Five hundred eighteen patients who received a medical emergency team call over 1 month. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 652 medical emergency team calls in 518 patients, with multiple calls in 99 (19.1%) patients. There were 161 (31.1%) patients with limitations of medical therapy during the study period. The limitation of medical therapy was instituted in 105 (20.3%) and 56 (10.8%) patients before and after the medical emergency team call, respectively. In 78 patients who died with a limitation of medical therapy in place, the last medical emergency team review was on the day of death in 29.5% of patients, and within 2 days in another 28.2%.Compared with patients who did not have a limitation of medical therapy, those with a limitation of medical therapy were older (80 vs. 66 yrs; p < .001), less likely to be male (44.1% vs. 55.7%; p = .014), more likely to be medical admissions (70.8% vs. 51.3%; p < .001), and less likely to be admitted from home (74.5% vs. 92.2%, p < .001). In addition, those with a limitation of medical therapy were less likely to be discharged home (22.4% vs. 63.6%; p < .001) and more likely to die in hospital (48.4% vs. 12.3%; p < .001). There was a trend for increased likelihood of calls associated with limitations of medical therapy to occur out of hours (51.0% vs. 43.8%, p = .089). CONCLUSIONS: Issues around end-of-life care and limitations of medical therapy arose in approximately one-third of calls, suggesting a mismatch between patient needs for end-of-life care and resources at participating hospitals. These calls frequently occur in elderly medical patients and out of hours. Many such patients do not return home, and half die in hospital. There is a need for improved advanced care planning in our hospitals, and to confirm our findings in other organizations.
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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 | 0.003 | 0.031 |
| 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 | 0.000 |
| 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 | 0.000 |
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".