Carboplatin–paclitaxel-induced leukopenia and neuropathy predict progression-free survival in recurrent ovarian cancer
Bibliographic record
Abstract
BACKGROUND: We assess the prognostic value of chemotherapy-induced leukopenia and sensory neuropathy in the CALYPSO trial patients treated with carboplatin-paclitaxel (CP) or carboplatin-liposomal doxorubicin (CPLD). METHODS: We performed a landmark analysis at first month after randomisation to correlate leukopenia (nadir white blood cell <4.0 × 10(9) per litre or severe infection) during cycle 1 of chemotherapy with progression-free survival (PFS). Using time-dependent proportional-hazards models, we also investigated the association between neuropathy and PFS. RESULTS: Of 608 patients with nadir blood and did not receive growth factors, 72% (CP=70%, CPLD=73%) had leukopenia. Leukopenia was prognostic for PFS in those receiving CP (adjusted hazard ratio (aHR) 0.66, P=0.01). Carboplatin-liposomal doxorubicin was more effective than CP in patients without leukopenia (aHR 0.51, P=0.001), but not those experiencing leukopenia (aHR 0.93, P=0.54; interaction P=0.008).Of 949 patients, 32% (CP=62%, CPLD=28%) reported neuropathy during landmark. Neuropathy was prognostic for PFS in the CP group only (aHR 0.77, P=0.02). Carboplatin-liposomal doxorubicin appeared to be more effective than CP among patients without neuropathy (aHR 0.70, P<0.0001), but not those with neuropathy (aHR 0.96, P=0.81; interaction P=0.15). CONCLUSION: First-cycle leukopenia and neuropathy were prognostic for patients treated with CP. Efficacy of CP treatment was similar to CPLD in patients who developed leukopenia. These findings support further research to understand the mechanisms of treatment-related toxicity.
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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 | 0.002 |
| 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".