Peripheral Neuropathy Potentially Associated to Poly (ADP-Ribose) Polymerase Inhibitors: An Analysis of the Eudravigilance Database
Bibliographic record
Abstract
Poly (ADP-Ribose) polymerase inhibitors (PARPi) have emerged as a targeted therapy in cancer treatment with promising results in various types of cancer. This work aims to investigate the profile of adverse drug reactions (ADRs) associated with PARPi through the reports provided by the Eudravigilance (EV) database. We also intend to analyze the potential association of peripheral neuropathy to PARPi. Data on individual case safety reports (ICSRs) were obtained by accessing the European spontaneous reporting system via the EV website. A total of 12,762 ICSRs were collected from the EV database. Serious cases of nervous system disorders were analyzed providing strong evidence that peripheral neuropathy was reported in a higher frequency in patients treated with niraparib. Most cases reported a not recovered/not resolved outcome and involved drug withdrawal. However, several studies suggest that PARPi attenuate chemotherapy-induced painful neuropathy. Unexpected ADRs such as peripheral neuropathy may also occur, mostly in patients taking niraparib. Further pharmacovigilance studies should be conducted in this area to clarify with more precision the toxicity profile of these drugs.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".