Rise in post-resection neutrophil-to-lymphocyte ratio correlates with decreased survival in glioblastoma patients
Bibliographic record
Abstract
Abstract Background Neutrophil-to-lymphocyte ratio (NLR) is used in the prognostication of multiple malignancies. However, the NLR value in glioblastoma (GBM) is controversial. This controversy may be due to the unaccounted effect of dexamethasone on NLR. Using retrospective data from 230 isocitrate dehydrogenase-1 (IDH) wild-type GBM patients, we studied the prognostic value of NLR in relation to dexamethasone treatment in GBM. Methods We retrospectively analyzed 230 patients with GBM. NLR and dexamethasone use were used as dichotomous variables with cutoff values of 9.5 and 8 mg, respectively. Correlations between high NLR, as well as NLR change after surgery, and patient outcome measures, including post-surgical complications and survival, were assessed using Kaplan–Meier curves, logistic, and Cox regression analyses. Results We demonstrate in this study that high perioperative NLR (≥9.5 NLR) does not associate with survival of GBM patients (274 days, 95% confidence interval [CI] 211–337, vs. 229 days, 95% CI 52–406, P = .9). However, high positive change in NLR (≥6 units) (higher postoperative NLR relative to preoperative NLR) has a significant association with decreased survival in GBM patients (196 days, 95% CI 121–270, vs. 304 days, 95% CI 223–384, P = .01). High preoperative and perioperative average dexamethasone (≥8 mg) treatment did not change the perioperative NLR trend and were not associated with decreased survival. Conclusions We demonstrate that an increase in NLR after surgery associates with decreased GBM patient survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".