Development of an evidence-based clinical pathway for patients undergoing pancreaticoduodenectomy.
Bibliographic record
Abstract
130 Background: Pancreas cancer is the 4th cause of cancer death. Surgical resection is the optimal treatment. But pancreaticoduodenectomy (PD) is complex with high perioperative morbidity. Complications following PD have a negative effect on quality of life and survival. Clinical pathways (CPW) are quality improvement (QI) tools that standardize the processes of care. Our aim was to develop and implement an evidence-based CPW for PD in the province of Ontario, Canada. Methods: A CPW following PD was developed using the Knowledge-to-Action framework. All 9 high volume Hepato-Pancreato-Biliary (HPB) centers in Ontario were invited to participate in a needs assessments workshop to promote awareness and early agreement for a CPW. Enthusiasm for a CPW was confirmed. End user input was incorporated into a multidisciplinary CPW. Barriers and enablers to implementation were assessed. Evidentiary support for CPW elements was integrated into the final product. An active implementation strategy that addressed key barriers was developed to promote CPW adoption and adherence. Pilot testing was undertaken. CPW uptake, compliance, and impact on clinical outcomes were evaluated. Pilot results were used to design the final CPW product and implementation strategy. Results: The needs assessment confirmed variability of perioperative processes at all sites. Participants expressed interest for a CPW and consensus was obtained on essential CPW components. During a 15 month pilot phase, 83/122 (68%) of eligible patients were initiated on CPW. Subjects able to achieve the goals of the CPW had a shorter length of stay (7 vs 11 days), lower in-hospital complications (9, 22% vs 19, 45%) and readmission (6, 15% vs 11, 26%) compared to those who were unable to achieve CPW targets. The majority of CPW users reported that the CPW was easy to use (26, 93%) and did not adversely affect workload (20, 71%). Pilot data were used to finalize the strategy used to implement the CPW at all Ontario HPB centers. Conclusions: We report that introduction of a standardized CPW for complex cancer surgery is possible with early engagement of stakeholders. Initial results suggest that this standardized approach is safe and positively impact quality of surgical care.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".