Is there a relationship between repopulation and hypoxia/reoxygenation? Results from human carcinoma of the cervix
Bibliographic record
Abstract
Long overall treatment times are detrimental for cure by radiotherapy and it has been argued that this may be due to repopulation occurring during the course of treatment. However, attempts to predict treatment outcome in relation to tumour proliferation, using pretreatment measurements of kinetic parameters such as Tpot or labelling index (LI) have not met with great success. One possible reason is that hypoxia/reoxygenation is linked to the growth of the tumour and its ability to repopulate. Data from studies in animal models have provided support for this possibility. We made measurement of tumour hypoxia, reoxygenation during treatment and pretreatment measurements of both Tpot and LI in groups of patients with cervix carcinoma undergoing radical radiation treatment. The data show a relationship between pretreatment pO2 measurements and treatment outcome, but reoxygenation did not show any association with treatment outcome. There was no significant association between pretreatment kinetic parameters and treatment outcome, nor was there any evidence of a relationship between pretreatment kinetic parameters and pO2. In the small group of 28 patients whose tumours underwent measurements of both pretreatment kinetic parameters (Tpot, LI) and reoxygenation, there was no relationship between these two sets of measurements. There was also no evidence that a combination of kinetic and reoxygenation measurements could be predictive of treatment outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".