Oral Fluoroquinolone Prescribing to Children in the United States From 2006 to 2015
Bibliographic record
Abstract
BACKGROUND: Fluoroquinolone (FQ) prescription rates have increased over the last 10 years despite recent warnings of serious adverse effects such as peripheral neuropathy and tendinopathy. Currently, there are no published data on the extent or appropriateness of FQ prescribing in children. METHODS: Drug prescription data from the PharMetrics Plus health claims database (United States) were analyzed to examine dispensing of ciprofloxacin, levofloxacin, moxifloxacin, ofloxacin, or gemifloxacin to children from 2006 to 2015. Based on American Academy of Pediatrics recommendations, an algorithm was created to quantify inappropriate FQ prescriptions, which was further stratified by age and FQ type. RESULTS: Among a cohort of 2,754,431 children, 372,357 prescriptions for an oral FQ were dispensed between 2006 and 2015. An increase was observed in FQ prescriptions from 2006 to 2013, with numbers coming down in 2014 and 2015. Ciprofloxacin was the most frequently prescribed FQ (334,268 prescriptions) followed by levofloxacin (19,386), moxifloxacin (18,434) and combined ofloxacin/gemifloxacin prescriptions (369). Of the FQ prescriptions in children, 48% were prescribed to those 10 years of age or younger, and 22% were deemed inappropriate. CONCLUSIONS: Our study suggests an increase in the prescribing of FQs, mostly ciprofloxacin, over a 10-year period, although numbers have decreased slightly in 2014 and 2015. At least 1 in 5 prescriptions were deemed unnecessary. In light of recent FQ safety warnings and lack of long-term safety data with FQ use in children and potential risk of increasing antibiotic resistance, clinicians are advised to refrain from using FQs for uncomplicated community-acquired infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".