Nonsteroidal Anti-Inflammatory Drugs: A survey of practices and concerns of pediatric medical and surgical specialists and a summary of available safety data
Bibliographic record
Abstract
OBJECTIVES: To examine the prescribing habits of NSAIDs among pediatric medical and surgical practitioners, and to examine concerns and barriers to their use. METHODS: A sample of 1289 pediatricians, pediatric rheumatologists, sports medicine physicians, pediatric surgeons and pediatric orthopedic surgeons in the United States and Canada were sent an email link to a 22-question web-based survey. RESULTS: 338 surveys (28%) were completed, 84 were undeliverable. Of all respondents, 164 (50%) had never prescribed a selective cyclooxygenase-2 (COX-2) NSAID. The most common reasons for ever prescribing an NSAID were musculoskeletal pain, soft-tissue injury, fever, arthritis, fracture, and headache. Compared to traditional NSAIDs, selective COX-2 NSAIDs were believed to be as safe (42%) or safer (24%); have equal (52%) to greater efficacy (20%) for pain; have equal (59%) to greater efficacy (15%) for inflammation; and have equal (39%) to improved (44%) tolerability. Pediatric rheumatologists reported significantly more frequent abdominal pain (81% vs. 23%), epistaxis (13% vs. 2%), easy bruising (64% vs. 8%), headaches (21% vs. 1%) and fatigue (12% vs. 1%) for traditional NSAIDs than for selective COX-2 NSAIDs. Prescribing habits of NSAIDs have changed since the voluntary withdrawal of rofecoxib and valdecoxib; 3% of pediatric rheumatologists reported giving fewer traditional NSAID prescriptions, and while 57% reported giving fewer selective COX-2 NSAIDs, 26% reported that they no longer prescribed these medications. CONCLUSIONS: Traditional and selective COX-2 NSAIDs were perceived as safe by pediatric specialists. The data were compared to the published pediatric safety literature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".