A Clinical Investigation of the Association between Perioperative Oral Management and Prognostic Nutritional Index in Patients with Digestive and Urinary Cancers
Bibliographic record
Abstract
Background: The prognostic nutritional index (PNI) is a simple metric calculated using serum albumin and the peripheral lymphocyte count. It was reported that a low PNI score is significantly associated with major postoperative complications and poor prognosis. The purpose of the present study was to investigate the effects of perioperative oral management (POM) on the perioperative PNI profiles of patients with digestive system or urinary cancers. Study Design: The medical records of 181 patients with cancer who underwent surgery and for whom a PNI could be calculated were retrospectively reviewed. Results: The intervention rate with POM was 34.8%. The median preoperative PNI score was 48.25 in all patients with a POM intervention [25% to 75% interquartile range (IQR): 44.38–54.13] and 47.25 in those without an intervention (IQR: 42.0–53.5). Compared with patients not receiving POM, those who received POM had significantly higher PNI scores from the early postoperative period (p < 0.05). Notably, of patients who could resume oral intake within 3 days after surgery, those who received POM intervention, compared with those who did not, had significantly higher PNI scores from the early postoperative period (p < 0.05). Conclusions: Perioperative oral management interventions might have positive effects on the postoperative PNI scores of patients with cancer.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".