Patient optimization for gastrointestinal cancer surgery
Bibliographic record
Abstract
BACKGROUND: Although surgical resection remains the central element in curative treatment of gastrointestinal cancer, increasing emphasis and resource has been focused on neoadjuvant or adjuvant therapy. Developments in these modalities have improved outcomes, but far less attention has been paid to improving oncological outcomes through optimization of perioperative care. METHODS: A narrative review is presented based on available and updated literature in English and the authors' experience with enhanced recovery research. RESULTS: A range of perioperative factors (such as lifestyle, co-morbidity, anaemia, sarcopenia, medications, regional analgesia and minimal access surgery) are modifiable, and can be optimized to reduce short- and long-term morbidity and mortality, improve functional capacity and quality of life, and possibly improve oncological outcome. The effect on cancer-free and overall survival may be of equal magnitude to that achieved by many adjuvant oncological regimens. Modulation of core factors, such as nutritional status, systemic inflammation, and surgical and disease-mediated stress, probably influences the host's immune surveillance and defence status both directly and through reduced postoperative morbidity. CONCLUSION: A wider view on long-term effects of expanded or targeted enhanced recovery protocols is warranted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".