Contact Dermatitis and Bradycardia in a Preterm Infant Given Tetracaine 4% Gel
Bibliographic record
Abstract
The use of analgesics for procedural pain management in the newborn infant has been steadily increasing during the past decade. With this trend of increased analgesic utilization, there is the potential for infants to suffer from drug-induced side effects. There also is the potential to wrongfully blame drugs for all adverse events that occur during analgesic use. Two adverse events that occurred in a neonate exposed to tetracaine gel and the probability that the adverse events were caused by the drug are presented. During administration of the topical local anesthetic tetracaine for analgesia during percutaneous central venous catheter placement, a preterm infant experienced bradycardia. Several hours later, a local cutaneous reaction that progressed to skin desquamation occurred at the site. The authors assessed the probability that tetracaine caused 2 adverse events using a validated adverse drug reaction probability scale by Naranjo et al. According to the algorithm developed by Naranjo et al, it was determined that bradycardia was unlikely caused by the drug; however, the dermal reaction was probably the result of the drug.The authors determined that tetracaine caused a serious local skin reaction, but not bradycardia, in a preterm infant. This is the first report of a serious skin reaction in a neonate treated with tetracaine. Based on these findings, tetracaine gel can continue to be used to treat pain in neonates with careful evaluation of the skin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".