Impact of Unplanned Intra‐Operative Conversions on Outcomes in Minimally Invasive Pancreatoduodenectomy
Bibliographic record
Abstract
Abstract Background Minimally‐invasive pancreatoduodenectomy (MIPD) is fraught with the risk of complication‐related deaths (LEOPARD‐2), a significant volume‐outcome relationship and a long learning curve. With rates of conversion for MIPD approaching 40%, the impact of these on overall patient outcomes, especially, when unplanned, are yet to be fully elucidated. This study aimed to compare peri‐operative outcomes of (unplanned) converted MIPD against both successfully completed MIPD and upfront open PD. Methods A systematic review of major reference databases was undertaken. The primary outcome of interest was 30‐day mortality. Newcastle–Ottawa scale was used to judge the quality of the studies. Meta‐analysis was performed using pooled estimates, derived using random effects model. Results Six studies involving 20,267 patients were included in the review. Pooled analysis demonstrated (unplanned) converted MIPD were associated with an increased 30‐day (RR 2.83, CI 1.62‐ 4.93, p = 0.0002, I2 = 0%) and 90‐day (RR 1.81, CI 1.16‐ 2.82, p = 0.009, I2 = 28%) mortality and overall morbidity (RR 1.41, CI 1.09; 1.82, p = 0.0087, I2 = 82%) compared to successfully completed MIPD. Patients undergoing (unplanned) converted MIPD experienced significantly higher 30‐day mortality (RR 3.97, CI 2.07; 7.65, p < 0.0001,I2 = 0%), pancreatic fistula (RR 1.65, CI 1.22‐ 2.23, p = 0.001, I2 = 0%) and re‐exploration rates (RR 1.96, CI 1.17‐ 3.28,p = 0.01, I2 = 37%) compared upfront open PD. Conclusions Patient outcomes are significantly compromised following unplanned intraoperative conversions of MIPD when compared to successfully completed MIPD and upfront open PD. These findings stress the need for objective evidence‐based guidelines for patient selection for MIPD.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".