Risk of serious skin and subcutaneous tissue disorders for nimesulide among the pediatric population: a jeopardy identified through the analysis of global individual case safety reports
Bibliographic record
Abstract
Background The safety reports arising currently on nimesulide are divulging the jeopardy of skin and subcutaneous tissue disorders (SSTDs).Research Design and Methods The global individual case safety reports on nimesulide-induced SSTDs available at VigiBase® were analyzed up to 31 March 2023. Disproportionality analyses viz. Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), and Information Component (IC) were performed to identify the quantitative signals.Results Out of 33,983,649 de-duplicated cases available in the VigiBase®, 1,664,134 (4.9%) were in pediatrics below 12 years of age. Among these, cases attributed to nimesulide were 251, of which 126 (50.2%) were on SSTDs. Among all the SSTDs reported for nimesulide, the serious reactions like urticaria [PRR = 2.3; lower bound (LB) ROR = 1.7; IC025 = 0.6], Stevens-Johnson syndrome (SJS) [PRR = 28.3; LB ROR = 18.2; IC025 = 3.2], angioedema [PRR = 7.5; LB ROR = 4.5; IC025 = 1.7], and toxic epidermal necrolysis (TEN) [PRR = 27.4; LB ROR = 11.5; IC025 = 1.5] were identified as potential signals. In comparison with non-SSTDs, SSTDs reported for nimesulide were significantly higher among children (2–11 years, 90.5%), from India (38.9%), and by the physician (60.3%).Conclusions Identifying the giant quantitate association between nimesulide and serious & life-threatening reactions like SJS and TEN, precautionary measures need to be taken by the regulatory authorities to prevent nimesulide-induced SSTDs among the pediatric 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".