Identifying Patterns of Primary Care Antibiotic Prescribing for a Spinal Cord Injury (SCI) Cohort Using an Electronic Medical Records (EMR) Database
Bibliographic record
Abstract
Background Individuals with a spinal cord injury (SCI) are considered higher users of antibiotics. However, to date there have been no detailed studies investigating outpatient antibiotic use in this population. Objectives (1) To describe primary care antibiotic prescribing patterns in adults with SCI rostered to a primary care physician (PCP), and (2) to identify patient or PCP factors associated with number of antibiotics prescribed and antibiotic prescription duration. Methods A retrospective cohort study using linked health administrative and electronic medical records (EMR) databases from January 1, 2013 to December 31, 2015 among 432 adults with SCI in Ontario, Canada. Negative binomial regression analyses were conducted to identify patient or physician factors associated with number of antibiotics prescribed and prescription duration. Results During the study period, 61.1% of the SCI cohort received an antibiotic prescription from their PCP. There were 59.8% of prescriptions for urinary tract infections (UTI) and 24.6% of prescriptions for fluoroquinolones. Regression analysis found catheter use was associated with increased number of antibiotics prescribed (relative risk [RR] = 3.1; 95% CI, 2.3-4.1; p < .001) and late career PCPs, compared to early-career PCPs, prescribed a significantly longer duration (RR = 1.8; 95% CI, 1.1-3.1; p = .02). Conclusion UTIs were the number one prescription indication, and fluoroquinolones were the most prescribed antibiotic. Catheter use was associated with number of antibiotics, and PCP's years of practice was associated with duration. The study provided important information about primary care antibiotic prescribing in the SCI population and found that not all individuals received frequent antibiotics prescriptions.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".