Neoadjuvant treatment: A window of opportunity for nutritional prehabilitation in patients with pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Patients affected by pancreatic ductal adenocarcinoma (PDAC) frequently present with advanced disease at the time of diagnosis, limiting an upfront surgical approach. Neoadjuvant treatment (NAT) has become the standard of care to downstage non-metastatic locally advanced PDAC. However, this treatment increases the risk of a nutritional status decline, which in turn, may impact therapeutic tolerance, postoperative outcomes, or even prevent the possibility of surgery. Literature on prehabilitation programs on surgical PDAC patients show a reduction of postoperative complications, length of hospital stay, and readmission rate, while data on prehabilitation in NAT patients are scarce and randomized controlled trials are still missing. Particularly, appropriate nutritional management represents an important therapeutic strategy to promote tissue healing and to enhance patient recovery after surgical trauma. In this regard, NAT may represent a new interesting window of opportunity to implement a nutritional prehabilitation program, aiming to increase the PDAC patient's capacity to complete the planned therapy and potentially improve clinical and survival outcomes. Given these perspectives, this review attempts to provide an in-depth view of the nutritional derangements during NAT and nutritional prehabilitation program as well as their impact on PDAC patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".