EVALUATION OF SIGNAL DETECTION FOR PLATINUM COMPOUNDS IN CANADIAN SPONTANEOUS ADVERSE EVENT REPORTS
Bibliographic record
Abstract
Objective: The objective of the study was to identify possible toxic signal induced by cisplatin and carboplatin treatment by searching database from Canadian Adverse Reaction Monitoring Program (CADRMP). Methods: Adverse drug reaction (ADR) signal detection were measured by methods such as, the proportional reporting ratio (PRR); reporting odds ratio (ROR); the chi-square statistics method; the 95% confidence interval (CI); the observed to expected (O/E) ratio and Du Mouchel method calculated PRR. Signal detection was also performed by applying Bayesian Confidence Propagation Neural Network Method (BCPNN). Calculated statistics by different methods were compared with regulatory criteria of a statistics value ≥ 4.0 for chi-square statistics and ≥3.0 for the rests for signal detection to be declared as significant. Results: For cisplatin, the PRR was found to be 53.44; by the Du Mouchel Method it was 20.7977; the chi-square statistics was 544.70, whereas the lower and upper limits of 95% CI of PRR was found to be 4.57 and 3.67, respectively. The O/E ratio was found to be 20.9130 and ROR was found to be 55.03. For carboplatin, the PRR was found to be 7.04412; by the Du Mouchel Method it was 16.4360; the chi-square statistics were 623.36645, whereas the lower and upper limits of 95% CI of PRR was found to be 3.6475 and 2.9167, respectively. The O/E ratio was found to be 16.43854 and reporting odds ratio was found to be 7.6065. By BCPNN method, the value of information components (IC) is 4.4031 for cisplatin means middle signal for cisplatin-induced ototoxicity. However, the value of IC is 2.4851 for carboplatin means middle signal for pruritis. Conclusion: The therapeutic class specific signal of ototoxicity coupled with cisplatin and of pruritis coupled with carboplatin was found significant enough to induce ototoxicity and pruritis respectively in the Canadian population.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".