A retrospective review of the real-world experience of the Pegfilgrastim biosimilar (Lapelga®) to the reference biologic (Neulasta®)
Bibliographic record
Abstract
INTRODUCTION: Cancer patients receiving myelosuppressive chemotherapy are vulnerable to febrile neutropenia (FN) which contributes to poor treatment outcomes. The use of granulocyte colony-stimulating factors is administered to prevent chemotherapy-induced neutropenia. The introduction of biosimilars has allowed for greater cost-savings while maintaining safety and efficacy. This retrospective study assessed the incidence of FN and related treatment outcomes and the cost minimization of a pegfilgrastim biosimilar and its reference. METHODS: A retrospective chart review of breast cancer patients receiving (neo) adjuvant chemotherapy from February 2017 to May 2020 was conducted. The endpoints included the incidence of FN, the occurrence of dose reduction (DR), dose delay (DD) and pain. A cost minimization analysis was performed from a third-party payer perspective. RESULTS: One hundred Neulasta® and 74 Lapelga® patients were included in the first-cycle analysis. The rate of FN in cycle 1 for Neulasta® and Lapelga® was 2/100 and 4/74, respectively; risk difference (RD) = 3.4%; 95% CI: -2.4 to 9.2%. Eighty-three Neulasta® and 59 Lapelga® patients were included in the all-cycle analyses, where DR was reported in 76 (15%) Neulasta® cycles vs 33 (10%) Lapelga® cycles (RD = -3.6, 95% CI: -10.2 to 2.9). DD was reported in 20 (4%) Neulasta® cycles vs. 11 (3.5%) Lapelga® cycles (RD = -0.3; 95% CI: -2.7 to 2.0). Adverse events were similar between groups. Cost minimization using a cohort of 20,000 patients translated into an incremental savings of $21,606,800 CAD for each cycle. CONCLUSION: The biosimilar pegfilgrastim was non-inferior to the reference biologic based on FN incidence in addition to related outcomes including DR and DD.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".