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Record W3016034466 · doi:10.1093/cid/ciz713

Reply to Hughes et al

2019· letter· fr· W3016034466 on OpenAlexaff
César I. Fernández-Lázaro, Bradley J. Langford, Kevin L. Schwartz

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typeletter
Languagefr
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsSt Joseph's Health CentreUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsPhilosophyPsychology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.163
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.052
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0100.006
Scholarly communication0.0100.005
Open science0.0040.004
Research integrity0.1630.069
Insufficient payload (model declined to judge)0.0180.013

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.

Opus teacher head0.053
GPT teacher head0.404
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractno

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