Population-Based Study of the Increased Incidence of Skin and Soft Tissue Infections and Associated Antimicrobial Use
Bibliographic record
Abstract
Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has spread rapidly throughout the world in the last decade. We sought to demonstrate the impact of the emergence of CA-MRSA in Western Canada on physician visits, incision-and-drainage procedures, and antibiotic prescribing for skin and soft tissue infections (SSTI). We used the provincial physician billing system to determine the rate of physician visits (per 1,000 population per year) of SSTI and incision-and-drainage procedures. A database capturing all outpatient prescriptions in the province was anonymously linked to associated physician billing codes to quantify prescriptions associated with SSTI. Antibiotic prescriptions (overall and class specific) were expressed as their defined daily dose (DDD) per 1,000 inhabitants per day. Between 1996 and 2008, the rate of visits for all SSTI increased by 15%, and the majority of visits did not include an incision-and-drainage procedure. The rate of antibiotic prescribing for SSTI increased by 49%. The majority of this increase was attributable to the higher rates of use of clindamycin (627%), trimethoprim-sulfamethoxazole (380%), cephalosporins (160%), and amoxicillin-clavulanate (627%). Health care utilization and antibiotic prescribing rates for SSTI, but not incision-and-drainage procedures, have increased in association with the CA-MRSA epidemic. While much of the increase in antibiotic use reflects an appropriate change to trimethoprim-sulfamethoxazole, there is room for education regarding the limitations of cephalosporins and clindamycin, given current susceptibility profiles.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".