Reply to Hughes et al
Bibliographic record
Abstract
To the Editor—We thank Hughes et al [1] for their interest in our study [2] and thoughtful commentary on some of the important limitations in the clinical trials cited by Wald-Dickler and Spellberg [3]. One of these limitations, as aptly pointed out by Hughes et al, is that for patients to be randomized and therefore included in the trials, they had to demonstrate clinical improvement prior to randomization. Patients with cellulitis who are not improving by day 5 should clearly not be candidates for 5 days of therapy. Translating trial data into clinical practice is often challenging and “indication creep” can have serious consequences [4]. It is critical that clinicians appreciate the sometimes limited inclusion criteria when incorporating clinical trial data into their practice. Follow-up should be part of routine outpatient care, which allows clinicians to reassess the diagnosis in patients not improving, and advise stoppage of therapy for those improved. Reducing unnecessarily prolonged duration of antibiotic treatment can have a major impact for antimicrobial stewardship efforts. In a recent study by Pouwels et al [5], it was shown that an estimated 1.3 million excess antibiotic days are being used in the United Kingdom due to antibiotic durations beyond what is recommended in local guidelines. These excess days likely have real harms to patients and contribute to antibiotic resistance [6]. The majority of antibiotic courses dispensed in outpatient settings are prescribed for common respiratory conditions and uncomplicated urinary tract infections [7]. Although personalization of duration of therapy is important, the majority of patients presenting with these uncomplicated community-acquired infections can be treated with 7 days of therapy or less [8]. As a result, in our study we conservatively selected a threshold of 8 days to differentiate short-course from long-course treatment at a population level, recognizing that there will be occasional exceptions when longer courses are indeed required. In our study we observed high interphysician variability in the proportion of antibiotic treatment durations, with 35% of the antibiotic courses exceeding 8 days of treatment. The largest predictor of prolonged durations was the number of years since the physician graduated from medical school [2]. Certainly, a proportion of these longer duration prescriptions are appropriate; however, the variability and sheer volume suggest there is much room for improvement. We are not advocating for fixed short durations for all, but for a cultural change away from arbitrarily long fixed durations [9], utilizing the best available evidence to offer individualized patient care. We support the commentary by Hughes et al that a condition-specific approach is flawed and instead we need large pragmatic patient-centered trials evaluating the safety and efficacy of stopping antibiotics at predefined clinical endpoints, such as symptomatic improvement. In the meantime, we recommend that clinicians prescribe antibiotics for the shortest evidence-based duration of therapy and abandon the common misconception that patients need to “complete the entire course of antibiotics” even if they feel better [10]. Instead, our efforts should focus on ensuring clinicians and patients appreciate the risks of unnecessarily prolonged courses of antibiotic therapy. Potential conflicts of interest. The author: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.163 | 0.069 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".