Assessment of tumor cell repopulation after chemotherapy for advanced ovarian cancer: Pilot study
Bibliographic record
Abstract
BACKGROUND: Repopulation of clonogenic tumor cells appears to increase during fractionated radiation treatment and is recognized as an important factor affecting local control. Given the longer intervals between cycles and longer total duration of treatment, the impact of repopulation is likely to be greater after chemotherapy. METHODS: We assessed tumor cell repopulation with the proliferative marker Ki-67 in 21 patients with ovarian carcinoma who received initial chemotherapy. Paraffin slides were evaluated from the diagnostic biopsy and from tumor obtained at debulking surgery after chemotherapy. Immunohistochemistry using the MIB-1 antibody was performed on the paired samples and analyzed with a digital imaging device linked to a color camera mounted on a transmitted-light microscope. The ratio of Ki-67 positive to all nuclei was used as a proliferative index and compared for pre- and postchemotherapy specimens. RESULTS: All patients received platinum-based chemotherapy and most showed a response to treatment. The median duration between last chemotherapy and debulking surgery was 33 days (range, 22-50 days). Four (19%) of 21 patients showed an increased proliferative index after chemotherapy, and the remainder showed a decrease (n = 12) or no significant change (n = 5). CONCLUSIONS: Our results did not suggest an increase in proliferation of tumor cells after this type of chemotherapy in the majority of patients with ovarian cancer.
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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".