Investigating the link between nutritional status and surgical outcomes in patients with liver or pancreatic cancer: a pilot study
Bibliographic record
Abstract
Investigating the link between nutritional status and surgical outcomes in patients with liver or pancreatic cancer: A pilot study The rise in the number of liver and pancreatic cancer patients in North America and around the world increases the need to better predict adverse outcomes after surgery. Because this patient population also presents with a higher rate of malnutrition which is known to increase the risks of morbidity and mortality, an effective, and validated, pre-operative nutritional assessment method is needed. This pilot study sought to evaluate the feasibility of various nutritional assessments in this patient population and to collect data on nutritional status in order to identify markers with which to develop future nutritional pre-habilitation programs that could positively impact postoperative outcomes. Results proved the feasibility and usefulness of the nutritional assessment in this patient population. However alleviating patient burden is key in facilitating subject recruitment and compliance. Most patients had a stable nutritional, however, the study also identified a subgroup of patients with worsening nutritional status while awaiting surgery; this group might be at an increased risk for postoperative adverse events. Further studies are needed to evaluate the absolute benefit of a preoperative nutritional rehabilitation program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".