Multicentre positive deviance to reduce adverse events and length of stay after pulmonary resection
Bibliographic record
Abstract
BACKGROUND: Postoperative adverse events (AEs) following pulmonary resection enormously impact patient well-being, length of stay (LOS) and healthcare costs. Standardised AE data collection can be used to identify positive outliers demonstrating positive deviance (PD) who may be helpful to inform the best practice. Here, we describe our initial experience of a novel quality improvement process using PD to reduce LOS and AEs. METHODS: AE rates and LOS were collected from four centres (2014-2020) using a common dictionary. Surgeons repeatedly participated in 60 to 90 min seminars consisting of the following process: identify outcome and procedure targeted, review relevant best evidence literature, view all data anonymised by surgeon or centre (if multicentre), choose and reveal identity of best performance PD outliers, who discuss their management principles while all receive self-evaluation reports, followed by collegial discussion to generate consensus recommendations, voted by all. We assessed overall impact on AEs and LOS using aggregate data in a before/after analysis. RESULTS: A total of 131 surgeons (average 12/seminar) participated in 11 PD seminars (8 local and 3 multicentre), yielding 85 consensus recommendation (average 8/seminar). Median LOS following lobectomy decreased from 4.0 to 3.0 days (p=0.04) following local PD seminars and from 4.0 to 3.5 days (p=0.11) following multicentre seminars. Trends for reductions in multiple AE rates were also observed. CONCLUSION: While limited by the longitudinal design, these findings provide preliminary support for this data-driven, collegial and actionable quality improvement process to help standardise and improve patient care, and merits further more rigorous investigation.
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 | 0.058 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".