Tumor Hypoxia Has Independent Predictor Impact Only in Patients With Node-Negative Cervix Cancer
Bibliographic record
Abstract
PURPOSE: This prospective clinical study was begun in 1994 to validate the independent prognostic impact of tumor hypoxia in patients with cervix cancer treated with definitive radiation therapy. PATIENTS AND METHODS: Between May 1994 and January 1999, 106 eligible patients with epithelial cervix cancer had tumor oxygen pressure (PO2) measured using the Eppendorf probe. Oxygenation data are presented as the hypoxic proportion, defined as the percentage of PO2 readings less than 5 mm/Hg (abbreviated as HP5) and the median PO2. RESULTS: The median HP5 in individual patients was 48%, and the median PO2 was HP5. Progression-free survival (PFS) for patients with hypoxic tumors (HP5 > 50%) was 37% at 3 years versus 67% in those patients with better oxygenated tumors (P = .004). In multivariate analysis, only tumor size (risk ratio [RR], 1.33; P = .0003) and evidence of pelvic nodal metastases on imaging studies (RR, 2.52; P = .0065) were predictive of PFS. However, an interaction between nodal status and oxygenation was observed (P = .006), and further analysis indicated that HP5 was an independent predictor of outcome in patients with negative nodes on imaging (P = .007). There was a significant increase in the 3-year cumulative incidence of distant metastases in the hypoxic group (41% v 15% in those with HP5 < 50%; P = .0023), but not in pelvic relapse (37% v 27%; P = .12). CONCLUSION: Tumor hypoxia is an independent predictor of poor PFS only in patients with node-negative cervix cancer, in addition to tumor size. Its impact appears to be related to an increased risk of distant metastases rather than to an effect on pelvic control.
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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".