397 Structural Characteristics Associated with Improved Outcomes in Gastrointestinal Cancer Surgery: A Systematic Review
Bibliographic record
Abstract
Abstract Background Centralisation of gastrointestinal (GI) cancer surgical services has corresponded with improvements in post-operative outcomes over the last two decades. However, centralisation has been variably implemented, and the specific structural factors that confer benefit remain unclear. This systematic review aimed to summarise current evidence on the structural characteristics associated with quality outcomes following surgery for cancers of the GI tract. Method A systematic search of the Embase, MEDLINE, and Cochrane databases was performed to identify studies that evaluated the importance of structure, or individual structural characteristics, in GI cancer surgery. These were categorised into one of: volume/centralisation, hospital infrastructure, personnel/staffing, or standardised care protocols. Outcomes of interest included mortality, morbidity, length of hospital stay, failure to rescue, readmission and cost. Results Sixty studies were included in the review. Greater hospital, surgeon and anaesthetic operative volume were each associated with improved outcomes. Protocols such as enhanced recovery after surgery contributed to faster peri-operative recovery and shorter hospital length of stay. While evidence on optimal post-operative care team composition was limited, geriatric co-management, physiotherapy, and dietetic input were each found to be important. Conclusions Although increased operative volume following centralisation is associated with improved surgical outcomes, definitions of ‘high-volume’ are variable and operation-specific volume thresholds have yet to be defined. Additional research to establish the ideal demographics of the peri-operative care team is also needed. This likely comprises surgeons, peri-operative physicians, specialist anaesthetists, dieticians, and physiotherapists, with additional on-site support from specialties such as interventional radiology and gastroenterology.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".