Inflammatory Indexes as Predictive Biomarkers of Postoperative Complications in Oncological Thoracic Surgery
Bibliographic record
Abstract
The role of inflammatory responses in predicting outcomes in oncological thoracic surgery is still unclear. The aim of this study was to evaluate a series of blood count inflammation indexes as predicting factors for postoperative complications. We retrospectively studied 249 patients undergoing elective thoracic surgery in our institution between 2008 and 2020. A total of 184 patients underwent open surgery, and 65 underwent VATS. The neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), and platelet-to-lymphocyte (PLR) ratios, Systemic Inflammation Response Index (SIRI) were calculated preoperatively and on the first and fourth postoperative days, as well as a new derivative index, the Aggregate Inflammation Systemic Index (AISI). Univariate correlations evidenced a statistically significant association between the NLR at the fourth postoperative day and the occurrence of surgical complications in the global cohort (rho = 0.15, p = 0.03). A similar significant association with MLR on the fourth postoperative day is found in the open group (rho = −0.15, p = 0.048). NLR and LMR on the fourth postoperative day are associated with postoperative complications in the whole and open groups, respectively. Simple, easy-to-perform and inexpensive, blood cell count indexes may be useful in predicting complications in oncological thoracic surgery. A greater number of broader, prospective, randomized studies are necessary to confirm these findings.
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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 | 0.001 | 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".