Outpatient Antibiotic Prescribing, Dispensing and Susceptibility Testing in a Primary Care Spinal Cord Injury Cohort
Bibliographic record
Abstract
Individuals with spinal cord injuries (SCI) frequently experience infections, such as urinary tract infections (UTIs), pressure ulcers and respiratory infections and as a result may frequently use antibiotics. Little research has been conducted on outpatient antibiotic prescribing in the SCI population, especially in primary care. The objectives of this thesis in a cohort of individuals with SCI rostered to a primary care physician (PCP) are to: 1) examine primary care antibiotic prescribing patterns; 2) determine patterns of urine culture susceptibility testing for UTI indicated antibiotic prescriptions; and 3) identify who prescribes outpatient antibiotics. This was a retrospective cohort study using linked electronic medical records and health administrative databases from Ontario. Study 1 described patterns of primary care antibiotic prescribing in a primary care rostered- SCI cohort of 432 individuals and found UTIs were the number one indication for prescriptions. Catheter use was found to be associated with number of antibiotics prescribed, and PCP years of practice was associated with prescription duration. Study 2, identifying urine culture testing and antibiotic prescribing patterns for UTIs in 432 individuals with SCI, found 58.1% of UTI-indicated antibiotic prescriptions were linked to a urine culture. Early-career physicians were more likely to order a urine culture when prescribing an antibiotic. Male physicians and international medical graduates were more likely to prescribe fluoroquinolone, than nitrofurantoin for UTIs. Study 3 identifies who prescribed outpatient antibiotics based on physician speciality for 320 individuals with SCI, who receive support from a drug benefit program. Nearly 60% of antibiotics were prescribed by physicians in an individual’s rostered-primary care practice, compared to 17.9% by emergency and non-rostered primary care physicians. Those who lived in urban and rural areas compared to suburban were more likely to receive antibiotics from emergency and non-rostered primary care physicians than physicians in their rostered-primary care practice. Results from these studies provide a better understanding of patterns of outpatient antibiotic prescribing and susceptibility testing in the SCI population, and can be used to inform future research, as well as develop antibiotic prescribing optimization strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".