Regional Collaborations as a Tool for Quality Improvements in Surgery
Bibliographic record
Abstract
In Brief Background: A systematic review of the literature identifying regional collaborations in surgical practice examining practices related to quality improvement. Methods: The MEDLINE, EMBASE, and Cochrane Library databases, were searched for published reports of regional collaborations in the surgical community relating to initiatives to enhance quality improvement, quality of care, patient safety, knowledge transfer, or communities of practice. Results: Seven collaborative initiatives met the inclusion criteria and were included in the systematic review of the evidence. Motivations for initiating collaborations were often in response to external demands for performance data. Changes in the processes of clinical care and improvements in clinical outcomes were reported on the basis of the collaborative efforts. Significant improvements in clinical outcomes such as decreases in mortality rates, lower duration of postoperative intubations, and fewer surgical-site infections were reported. Quality improvement process measures were also reported to be improved across all of the collaborative initiatives. Success factors included (a) the establishment of trust among health professionals and health institutions; (b) the availability of accurate, complete, relevant data; (c) clinical leadership; (d) institutional commitment; and (e) the infrastructure and methodological support for quality management. Conclusions: A community of practice framework incorporating the success elements described in the systematic review of the literature can be used as a valuable model for collaboration amongst surgeons and healthcare organizations to improve quality of care and foster continuing professional development. Results from the systematic review of the literature investigating models for collaboration among surgeons and healthcare organizations supports the idea that improving the quality of care and fostering continuing professional development is possible through the creation of communities of surgical practice based on identified success elements.
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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 | 0.118 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".