Safety and dose modification for patients receiving niraparib
Bibliographic record
Abstract
Background: Niraparib is a poly(ADP-ribose) polymerase inhibitor approved in the USA and Europe for maintenance treatment of adult patients with recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer who are in complete or partial response to platinum-based chemotherapy. In the pivotal ENGOT-OV16/NOVA trial, the dose reduction rate due to treatment-emergent adverse event (TEAE) was 68.9%, and the discontinuation rate due to TEAE was 14.7%, including 3.3% due to thrombocytopenia. A retrospective analysis was carried out to identify clinical parameters that predict dose reductions. Patients and methods: All analyses were carried out on the safety population, comprising all patients who received at least one dose of study drug. Patients were analyzed according to the study drug consumed (i.e., as treated). A predictive modeling method (decision trees) was used to identify important variables for predicting the likelihood of developing grade ≥3 thrombocytopenia within 30 days after the first dose of niraparib and determine cut-off points for chosen variables. Results: Following dose modification, 200 mg was the most commonly administered dose in the ENGOT-OV16/NOVA trial. Baseline platelet count and baseline body weight were identified as risk factors for increased incidence of grade ≥3 thrombocytopenia. Patients with a baseline body weight <77 kg or a baseline platelet count <150 000/µl in effect received an average daily dose ∼200 mg (median = 207 mg) due to dose interruption and reduction. Progression-free survival in patients who were dose reduced to either 200 or 100 mg was consistent with that of patients who remained at the 300 mg starting dose. Conclusions: The analysis presented suggests that patients with baseline body weight of <77 kg or baseline platelets of <150 000/µl may benefit from a starting dose of 200 mg/day. ClinicalTrials.gov ID: NCT01847274.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".