Bibliographic record
Abstract
A recent randomized controlled trial to evaluate the efficacy of a fixed, short antimicrobial treatment was published in May of this year (1). This interesting study brings to the fore the importance of evaluating, with proper methodology, one of the most disputable aspects of antimicrobial treatments, the duration. When supported by strong evidence or expert statements, shortening courses of anti-microbial treatments are an important component of antimicrobial stewardship programs (2). The course of therapy must be defined as the time period during which therapeutic concentrations are maintained at the site of infection, instead of the time during which an antimicrobial treatment is administered. This definition puts emphasis on the importance of a thorough evaluation before starting a short treatment. For example, the most recent Infectious Diseases Society of America (IDSA) skin and soft tissue guidelines suggest a treatment as short as five days for cellulitis (3). This duration might not apply to a patient with cellulitis and chronic arterial insufficiency of the lower limbs. Successful abbreviated treatment courses depend on several factors linked to host (immune status), pathogen (susceptibility, low spontaneous mutation rate, extracellular, rapid multiplication), infection site (accessible site, not as biofilm, no foreign body, not life-threatening, not in an abscess – low pH, or any other factors that inhibit antimicrobial action) and therapeutic agents (bactericidal, rapid onset of action, lack of propensity to induce mutants, good penetration in tissues, active against nondividing bacteria).
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".