Use of individualized starting dose and niraparib hematologic adverse event management costs in ovarian cancer
Bibliographic record
Abstract
Aim: To understand the impact of the niraparib individualized starting dose (ISD), compared with fixed starting dose (FSD), on the cost of hematologic adverse event (AE) management from a US payer perspective. Methods: The frequencies of grade ≥3 hematologic AEs that occurred in >1% of patients treated with niraparib were obtained from the primary analysis results of the phase III PRIMA/ENGOT-OV26/GOG-3012 trial. US unit costs for each grade ≥3 AE in the base case were obtained from the 2017 Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project database; unit costs were adjusted to 2020 US dollars. AE management costs per patient were calculated by multiplying AE unit cost by the frequency of each AE by niraparib starting dose. Because AEs were assumed to occur independently of one another, costs were added to derive the total cost. Results: For niraparib, the estimated AE management cost per patient was lower for the ISD than the FSD for all hematologic AEs (FSD vs ISD: thrombocytopenia, $4701.87 vs $1921.89; anemia, $2784.00 vs $1760.59; platelet count decreased, $2103.47 vs $922.51; neutropenia, $2112.50 vs $1369.56; neutrophil count decreased, $1285.87 vs $770.38). The total mean calculated AE management cost per patient was $12,987.71 with the FSD and $6744.93 with the ISD. Conclusion: For niraparib, the cost of managing hematologic AEs in the US was reduced by almost half with the ISD compared with the FSD. The cost reduction and improvements in safety associated with the niraparib ISD support its use in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".