Comparison of Reported Adverse Events for Two Plant-Derived Anti Cancer Therapeutics: Paclitaxel and Docetaxel
Bibliographic record
Abstract
The purpose of this research is to compare trends in reported adverse events for two plant-derived taxane drugs, paclitaxel versus docetaxel, and examine the types of adverse events associated with paclitaxel and docetaxel in clinical use.This research is novel, because it is the first to examine reported adverse events post-market for two anti-cancer therapeutics, paclitaxel and docetaxel, using a public regulatory database that contains reports from manufacturers, clinicians, and patients.The research reveals that the frequency of adverse events for paclitaxel has grown exponentially from 1991 to 2023.The number of adverse events reported for paclitaxel grew 14.6 times from the 530 reported adverse events in 1993 to the 8,294 reported adverse events in 2023.The reported adverse events from docetaxel sharply increased and peaked in 2018 as it grew 305%, from 3,697 reported adverse events in 2017 to 14,988 reported adverse events in 2018.Paclitaxel's most frequent reaction type is dyspnoea (shortness of breath) with 6,694 reported adverse events resulting in dyspnoea.The most frequent reaction type reported for docetaxel was alopecia (loss of hair), with 29,536 reported adverse events resulting in alopecia.Additionally, docetaxel is associated with psychological and hair-related adverse events, and it is significant to note that the most frequent reaction types reported for paclitaxel did not include either hair or psychological effects.It is also noteworthy that death is one of the top reported adverse events associated with paclitaxel, but not docetaxel.The results of this study are significant because they reveal that paclitaxel and docetaxel, despite being members of the same class of drugs, induce distinct adverse event types and patterns in patients.These findings may help physicians and patients select the best taxane drug, as well as anticipate and monitor potential side effects, especially life-threatening reactions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".