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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".