Rescue Improvement Conference
Bibliographic record
Abstract
OBJECTIVE: To understand the effectiveness of Rescue Improvement Conference, a forum that addresses FTR. SUMMARY OF BACKGROUND DATA: Every year over 150,000 patients die after elective surgery in the United States. FTR is the phenomenon whereby delayed recognition and/or response to serious surgical complications leads to a progressive cascade of adverse events culminating in death. Rescue Improvement Conference is an adapted version of the Ottawa-style morbidity and mortality conference, designed to address common contributors to FTR: ineffective communication and inadequate problem solving. METHODS: Mixed methods data were used to evaluate Rescue Improvement Conference, a bi-monthly forum that was first introduced in our academic medical center in 2018. Conference effectiveness data were collected via survey and open-text responses after 5 conferences between September 2018 and February 2020. We focused on 5 indicators of effectiveness: educational value, conference takeaways, discussion time, changes to surgical practice, and actionable opportunities for improvement. Twelve surgical faculty and house staff also provided feedback during semi-structured interviews. Qualitative data were analyzed using thematic analysis. RESULTS: Conference attendees (N = 140) felt that Rescue Improvement Conference was effective-all 5 indicators had mean scores above 5 on Likert scales. The qualitative data supports the quantitative findings, and 3 additional themes emerged: Rescue Improvement Conference enables the representation of diverse voices, promotes interdisciplinary collaboration, and encourages multilevel problem solving. CONCLUSIONS: Rescue Improvement Conference has the potential to support other surgical departments in developing system-level strategies to recognize and manage postoperative complications by providing stakeholders a forum to identify and discuss factors that contribute to FTR.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.016 |
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".