Textbook outcome and textbook oncological outcome in esophagogastric cancer surgery – A systematic scoping review
Bibliographic record
Abstract
INTRODUCTION: Quality assurance in esophagogastric surgery, particularly in an oncological context, is important, especially as long-term survival is highly affected by the short-term outcomes. Textbook Outcome (TO) and Textbook Oncological Outcome (TOO) serve as multidimensional metrics to assess surgical quality by evaluating various perioperative factors, as well as oncological outcomes. TO and TOO have been associated with improved long-term survival. AIM: This study aimed to examine the incidence of, and the definitions of TO and TOO used in esophagogastric oncological surgery. METHODS: This systematic scoping review followed the PRISMA 2020 guidelines and the PRISMA scoping review extension. The AMSTAR-2 was used to rate the review. A comprehensive systematic search was performed in Medline, Embase, and Web of Science and results were screened through Covidence. Quality assessment was conducted using the Newcastle-Ottawa scale. RESULTS: A total of 55 observational cohort studies on esophagogastric cancer surgery were included. A total of 245,075 patients was included in the assessment of the achievement of TO and TOO. The rate of TO achievement ranged from 20.4 to 84.2 %, while the rate of TOO achievement ranged from 21.3 to 57.6 %. TO and TOO definitions varied widely, combining a median of nine (range: 4-11) parameters with a total of 45 different parameters being reported. CONCLUSION: This systematic scoping review showed significant variations in incidence and in the definitions used for TO and TOO in esophagogastric cancer surgery between the included studies. This highlights the importance of standardizing the definitions of TO and TOO.
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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 | 0.023 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".