The evolving story of medical emergency teams in quality improvement
Bibliographic record
Abstract
Adverse events affect approximately 3% to 12% of hospitalized patients. At least a third, but as many as half, of such events are considered preventable. Detection of these events requires investments of time and money. A report in a recent issue of Critical Care used the medical emergency team activation as a trigger to perform a prospective standardized evaluation of charts. The authors observed that roughly one fourth of calls were related to a preventable adverse event, which is comparable to the previous literature. However, while previous studies relied on retrospective chart reviews, this study introduced the novel element of real-time characterization of events by the team at the moment of consultation. This methodology captures important opportunities for improvements in local care at a rate far higher than routine incident-reporting systems, but without requiring substantial investments of additional resources. Academic centers are increasingly recognizing engagement in quality improvement as a distinct career pathway. Involving such physicians in medical emergency teams will likely facilitate the dual roles of these as a clinical outreach arm of the intensive care unit and in identifying problems in care and leading to strategies to reduce them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.055 | 0.054 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".