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Record W4313332424 · doi:10.1093/ofid/ofac706

Can the Future of ID Escape the Inertial Dogma of Its Past? The Exemplars of Shorter Is Better and Oral Is the New IV

2022· article· en· W4313332424 on OpenAlexaff
Kusha Davar, Devin Clark, Robert M. Centor, Fernándo Domínguez, Bassam Ghanem, Rachael Lee, Todd C. Lee, Emily G. McDonald, Matthew C. Phillips, Parham Sendi, Brad Spellberg

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineIntensive care medicineRandomized controlled trialBacteremiaClinical PracticeClinical trialEndocarditisAlternative medicineAntibioticsInternal medicineFamily medicinePathology

Abstract

fetched live from OpenAlex

Like all fields of medicine, Infectious Diseases is rife with dogma that underpins much clinical practice. In this study, we discuss 2 specific examples of historical practice that have been overturned recently by numerous prospective studies: traditional durations of antimicrobial therapy and the necessity of intravenous (IV)-only therapy for specific infectious syndromes. These dogmas are based on uncontrolled case series from >50 years ago, amplified by the opinions of eminent experts. In contrast, more than 120 modern, randomized controlled trials have established that shorter durations of therapy are equally effective for many infections. Furthermore, 21 concordant randomized controlled trials have demonstrated that oral antibiotic therapy is at least as effective as IV-only therapy for osteomyelitis, bacteremia, and endocarditis. Nevertheless, practitioners in many clinical settings remain refractory to adopting these changes. It is time for Infectious Diseases to move beyond its history of eminent opinion-based medicine and truly into the era of evidenced-based medicine.

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)0.018
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.019
Scholarly communication0.0100.019
Open science0.0020.004
Research integrity0.0070.016
Insufficient payload (model declined to judge)0.0090.003

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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations74
Published2022
Admission routes1
Has abstractyes

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