Best Practices for Delivering Neoadjuvant Therapy in Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Importance: Neoadjuvant therapy (NT) is an increasingly used treatment strategy for patients with localized pancreatic ductal adenocarcinoma (PDAC). Little research has been conducted on cancer care delivery during NT, and the standards for optimal delivery of NT have not been defined. Objective: To develop consensus best practices for delivering NT to patients with localized PDAC. Design, Setting, and Participants: This study used a modified Delphi approach consisting of 2 rounds of voting, and a series of virtual conferences (from October to December 2023) to reach expert consensus on candidate best practice statements generated from a systematic review of the literature and expert opinion. An interdisciplinary panel was formed including 47 North American experts from surgical, medical, and radiation oncology, radiology, pathology, gastroenterology, integrative oncology, anesthesia, pharmacy, nursing, cancer care delivery research, and nutrition as well as patient and caregiver stakeholders. Main Outcome and Measures: Statements that reached 75% agreement or greater were included in final consensus statements. Results: Of the 47 participating panel members, 27 (57.64%) were male, and the mean (SD) age was 47.6 (8.2) years. Physicians reported completing training a mean (SD) 14.6 (8.6) years prior and seeing a mean (SD) 110.6 (38.4) patients with PDAC annually; 35 (77.7%) were in academic practice. Final consensus was reached on 82 best practices for delivering NT. Of these, 38 statements focused on pre-NT practices, including diagnosis and staging (n = 15), evaluation and optimization (n = 20), and decision-making (n = 3); 29 statements defined best practices during NT, including initiation (n = 3), delivery of therapy (n = 8), restaging practices (n = 12), and management of complications during NT (n = 6); and 15 best practices were identified to guide treatment post-NT, focusing on surgery (n = 7), pathology (n = 4), and follow-up (n = 3). Conclusions: Using a modified Delphi consensus technique, best practice guidelines were developed focusing on the optimal standards for delivering NT to patients with localized PDAC. Given the prognostic importance of completing multimodality therapy, efforts to standardize and optimize the delivery of NT represent an immediate opportunity to decrease care variation and improve outcomes for patients with PDAC. Future research should focus on validating and implementing best practice standards into clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".