At the Threshold: Defining Clinically Meaningful Resistance Thresholds for Antibiotic Choice in Community‐Acquired Pneumonia
Bibliographic record
Abstract
BACKGROUND: Community-acquired pneumonia caused by Streptococcus pneumoniae is a major source of morbidity and mortality. Macrolide antibiotics are recommended as empirical first-line therapy for patients with community-acquired pneumonia. Guidelines suggest a 25% rate of high-level macrolide resistance in the community as the threshold beyond which macrolides should not be used. We evaluated the implications of this threshold for clinical failure rates. METHODS: We developed a theoretical model linking the prevalence of macrolide resistance to patient outcomes, based on the epidemiological concept of risk difference. We estimated the risk of clinical failure as a function of the likelihood and impact of discordant therapy and of the probability of clinical failure even in the presence of optimal therapy. The model was parameterized on the basis of the best available data derived from the published medical literature, and clinical failures were valued monetarily using an expected net benefit approach. RESULTS: Under the proposed 25% resistance threshold, the risk difference for such therapy would be 1.2% (95% credible interval, 0.5%-3.1%) for death, 1.6% (95% credible interval, 0.5%-3.2%) for bacteremia, and 3.3% (95% credible interval, 1.1%-5.7%) for prolonged clinical course; excess risks of death were valued at >$10,000 per empirical treatment of community-acquired pneumonia and were further elevated in high-risk populations. Excluding low-level resistance resulted in a 4-fold underestimation of projected risks. CONCLUSION: A 25% resistance threshold that fails to consider low-level resistance will result in high excess rates of morbidity and mortality because of discordant therapy. Whether projected failure rates are classified as unacceptable is an important health policy question, because risk of clinical failure needs to be weighed against other considerations.
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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 | 0.054 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".