Fixed versus individualized treatment for five common bacterial infectious syndromes: a survey of the perspectives and practices of clinicians
Bibliographic record
Abstract
Background: Traditionally, bacterial infections have been treated with fixed-duration antibiotic courses; however, some have advocated for individualized durations. It is not known which approach currently predominates. Methods: We conducted a multinational clinical practice survey asking prescribers their approach to treating skin and soft tissue infection (SSTI), community-acquired pneumonia (CAP), pyelonephritis, cholangitis and bloodstream infection (BSI) of an unknown source. The primary outcome was self-reported treatment approach as being fully fixed duration, fixed minimum, fixed maximum, fixed minimum and maximum, or fully individualized durations. Secondary questions explored factors influencing duration of therapy. Multivariable logistic regression with generalized estimating equations was used to examine predictors of use of fully fixed durations. Results: Among 221 respondents, 170 (76.9%) completed the full survey; infectious diseases physicians accounted for 60.6%. Use of a fully fixed duration was least common for SSTI (8.5%) and more common for CAP (28.3%), BSI (29.9%), cholangitis (35.7%) and pyelonephritis (36.3%). Fully individualized therapy, with no fixed minimum or maximum, was used by only a minority: CAP (4.9%), pyelonephritis (5.0%), cholangitis (9.9%), BSI (13.6%) and SSTI (19.5%). In multivariable analyses, a fully fixed duration approach was more common among Canadian respondents [adjusted OR (aOR) 1.76 (95% CI 1.12-2.76)] and for CAP (aOR 4.25, 95% CI 2.53-7.13), cholangitis (aOR 6.01, 95% CI 3.49-10.36), pyelonephritis (aOR 6.08, 95% CI 3.56-10.39) and BSI (aOR 4.49, 95% CI 2.50-8.09) compared with SSTI. Conclusions: There is extensive practice heterogeneity in fixed versus individualized treatment; clinical trials would be helpful to compare these approaches.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".