The predictive value of nadir neutrophil count during treatment of cervical cancer: Interactions with tumor hypoxia and interstitial fluid pressure (IFP)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hypoxia, high interstitial fluid pressure (IFP) and immune effects have individually been shown to modulate radiotherapy (RT) response in cervical cancer. The aim of this study was to investigate the interplay between hypoxia or IFP and circulating neutrophil levels, and their combined effect on survival following RT. MATERIAL AND METHODS: A total of 287 FIGO stage IB to IIIB cervical cancer patients treated with RT or RT and cisplatin (RTCT) were included. Tumor hypoxia and IFP were measured at baseline prior to treatment. Absolute neutrophil count (ANC) was measured at baseline and weekly during treatment. Median follow up was 7.1 years. RESULTS: High nadir ANC at the point of maximal myelosuppression was a stronger predictor of inferior survival than high baseline ANC after adjusting for clinical prognostic factors and treatment (RT vs. RTCT). The predictive effect of nadir ANC was most evident in patients with well-oxygenated tumors or tumors with high IFP at diagnosis. CONCLUSIONS: This study provides new information about the combined influence of the tumor microenvironment and myeloid cells on the survival of cervical cancer patients treated with RT/RTCT to motivate the development of new treatments based on molecular targeting of immune-based radioresistance pathways.
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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 | 0.002 |
| 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".