Antibiotic treatment durations for pyogenic liver abscesses: A systematic review
Bibliographic record
Abstract
Background: We sought to systematically review the existing research on pyogenic liver abscesses to determine what data exist on antibiotic treatment durations. Methods: We conducted a systematic review and meta-analysis of contemporary medical literature from 2000 to 2020, searching for studies of pyogenic liver abscesses. The primary outcome of interest was mean antibiotic treatment duration, which we pooled by random-effects meta-analysis. Meta-regression was performed to examine characteristics influencing antibiotic durations. Results: Sixteen studies (of 3,933 patients) provided sufficient data on antibiotic durations for pooling in meta-analysis. Mean antibiotic durations were highly variable across studies, from 8.4 (SD 5.3) to 68.9 (SD 30.3) days. The pooled mean treatment duration was 32.7 days (95% CI 24.9 to 40.6), but heterogeneity was very high ( I 2 = 100%). In meta-regression, there was a non-significant trend towards decreased mean antibiotic treatment durations over later study years (−1.14 days/study year [95% CI −2.74 to 0.45], p = 0.16). Mean treatment duration was not associated with mean age of participants, percentage of infections caused by Klebsiella spp, percentage of patients with abscesses over 5 cm in diameter, percentage of patients with multiple abscesses, and percentage of patients receiving medical management. No randomized trials have compared treatment durations for pyogenic liver abscess, and no observational studies have reported outcomes according to treatment duration. Conclusions: Among studies reporting on antibiotic durations for pyogenic liver abscess, treatment practices are highly variable. This variability does not seem to be explained by differences in patient, pathogen, abscess, or management characteristics. Future RCTs are needed to guide optimal treatment duration for patients with this complex infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".