Surgical Process Improvement Tools: Defining Quality Gaps and Priority Areas in Gastrointestinal Cancer Surgery
Bibliographic record
Abstract
BACKGROUND: Surgery is a cornerstone of cancer treatment, but significant differences in the quality of surgery have been reported. Surgical process improvement tools (spits) modify the processes of care as a means to quality improvement (qi). We were interested in developing spits in the area of gastrointestinal (gi) cancer surgery. We report the recommendations of an expert panel held to define quality gaps and establish priority areas that would benefit from spits. METHODS: The present study used the knowledge-to-action cycle was as a framework. Canadian experts in qi and in gi cancer surgery were assembled in a nominal group workshop. Participants evaluated the merits of spits, described gaps in current knowledge, and identified and ranked processes of care that would benefit from qi. A qualitative analysis of the workshop deliberations using modified grounded theory methods identified major themes. RESULTS: The expert panel consisted of 22 participants. Experts confirmed that spits were an important strategy for qi. The top-rated spits included clinical pathways, electronic information technology, and patient safety tools. The preferred settings for use of spits included preoperative and intraoperative settings and multidisciplinary contexts. Outcomes of interest were cancer-related outcomes, process, and the technical quality of surgery measures. CONCLUSIONS: Surgical process improvement tools were confirmed as an important strategy. Expert panel recommendations will be used to guide future research efforts for spits in gi cancer surgery.
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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 | 0.114 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".