Bridging the gap between open and minimally invasive pancreaticoduodenectomy: the hybrid approach
Bibliographic record
Abstract
BACKGROUND: Minimally invasive pancreatic surgery has evolved rapidly, but total laparoscopic pancreaticoduodenectomy has not been widely adopted owing to its technical complexity. Hybrid laparoscopy-assisted pancreaticoduodenectomy (HLAPD) combines the relative ease of open surgery with the benefits of a minimally invasive approach. This study evaluates the safety and effectiveness of the hybrid approach compared with open surgery. METHODS: We retrospectively analyzed data of consecutive patients undergoing either hybrid or open pancreaticoduodenectomy (OPD) at our institution between September 2009 and December 2013. Demographic, operative and oncologic data were collected to compare outcomes between HLAPD and OPD. RESULTS: Our analysis included 33 patients (HLAPD: n = 13; OPD: n = 20). There were no differences in patient demographics, comorbidities or surgical indications. The HLAPD group had significantly lower intraoperative blood loss (450 mL v. 1000 mL, p = 0.023) and shorter length of hospital stay (8 v. 12 d, p = 0.025) than the OPD group. Duration of surgery did not differ significantly between the groups. There were no differences in postoperative analgesic requirements, Clavien grade I/II or grade III/IV complications or 90-day mortality. Oncologic outcomes showed no significant differences in tumour size, R1 resection rate or number of lymph nodes harvested. CONCLUSION: In select patients, HLAPD is a safe and effective procedure with comparable outcomes to conventional open surgery. Wider adoption of the hybrid approach will allow a greater number of patients to benefit from a less invasive procedure while facilitating the transition toward purely minimally invasive pancreaticoduodenectomy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".