Postoperative Natural Killer Cell Dysfunction: The Prime Suspect in the Case of Metastasis Following Curative Cancer Surgery
Bibliographic record
Abstract
Surgical resection is the foundation for the curative treatment of solid tumors. However, metastatic recurrence due to the difficulty in eradicating micrometastases remain a feared outcome. Paradoxically, despite the beneficial effects of surgical removal of the primary tumor, the physiological stress resulting from surgical trauma serves to promote cancer recurrence and metastasis. The postoperative environment suppresses critical anti-tumor immune effector cells, including Natural Killer (NK) cells. The literature suggests that NK cells are critical mediators in the formation of metastases immediately following surgery. The following review will highlight the mechanisms that promote the formation of micrometastases by directly or indirectly inducing NK cell suppression following surgery. These include tissue hypoxia, neuroendocrine activation, hypercoagulation, the pro-inflammatory phase, and the anti-inflammatory phase. Perioperative therapeutic strategies designed to prevent or reverse NK cell dysfunction will also be examined for their potential to improve cancer outcomes by preventing surgery-induced metastases.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".