1118. Trends of Paediatric Prescribing for Common Infections in British Columbia
Bibliographic record
Abstract
Abstract Background Antibiotic prescribing in pediatric care is highly prevalent, and quite often children are prescribed for conditions which are commonly self-limiting and viral in etiology. The purpose of this study was to examine the scope of pediatric antibiotic prescribing by indication, from 2013 to 2016, and identify potential new targets for provincial antimicrobial stewardship efforts. Methods Antibiotic prescription data for children were extracted from a provincial prescription database, and linked to physician billing data in order to obtain diagnostic information. Prescription rates were then calculated, and trends were examined by indication. Major categories included: upper respiratory tract infection, acute otitis media, lower respiratory tract, skin and soft tissue, and urinary tract infections. Results Our database included an average of 244,763 children per year, and 5,896,173 total antibiotic prescriptions. Increased indication-specific rates of prescribing were observed in children aged 0–2 years, for every category. Children aged 3–18 years experienced decreased prescribing across all indications, with the exception of urinary tract infections for those aged between 10–18 years. Urinary tract infections increased by 134% for children aged 0–2 years, and 75% for those aged 10–18 years, from 2013 to 2016. Although antibiotic use for upper respiratory tract infections decreased by 11% for all ages, these diagnoses continue to be prescribed for at rates 2 – 5 times higher than other conditions. Conclusion Although this study found a decrease in prescribing over time across all indications, antibiotic use continues to be a concern for upper respiratory tract infections in pediatric care. These diagnoses generally do not require antibiotics, and inappropriate prescribing is a major factor in antimicrobial resistance. The increased prescribing rates in the youngest age group (0–2 years) offers a new target for provincial stewardship efforts. Disclosures All authors: No reported disclosures.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".