Restrictive measure for the commercialization of antimicrobials in Brazil: results achieved
Bibliographic record
Abstract
OBJECTIVE: To assess whether the incidence of hospital infection by a resistant microorganism decreased after the implementation of the restrictive measure of the National Health Surveillance Agency for the commercialization of antimicrobials. METHODS: A historical cohort study of medical records of adult patients admitted to a general and public hospital from May 2010 to July 2011. A cohort was formed with patients admitted in the period before the restrictive measure for the commercialization of antimicrobials (Phase I) and a second cohort was formed with patients admitted after the implementation of the restrictive measure (Phase II). RESULTS: The instantaneous risk of hospital infection by a resistant microorganism was estimated at seven by 1,000 people-time (95%CI 0.006-0.008) in Phase I, and four by 1,000 people-time (95%CI 0.003-0.005) in Phase II of the study. The differences between the survival curves in the different phases of the study and stratified by age group were also significant (p < 0.05). CONCLUSIONS: The results suggest that the implementation of the restrictive measure of the commercialization of antimicrobials by the National Health Surveillance Agency reduced the incidence of hospital infection by a resistant microorganism.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".