Bibliographic record
Abstract
Reducing duration of antibiotic therapy without a dimin-u tion in effi cacy decreases cost, side eff ects, antibiotic related diarrhoea, and bacterial resistance. Havey and colleagues [1] reported the results of a systematic review and meta-analysis of antibiotic duration in bacteraemia and deduced short course therapy (<7 days) might be as eff ective as longer treatments. It is surprising given the obvious benefi ts and the frequency with which bacteraemia is documented in critically ill patients that there is such a paucity of randomised clinical trials (RCTs) comparing duration of therapy. Only one RCT, in neonates, had been performed in patients solely with bacteraemia. Accordingly, Havey and colleagues con-cluded that duration of antibiotic therapy in bacteraemia is poorly studied and would benefi t from a large RCT. Daneman and colleagues [2] performed a survey of Canadian infectious disease and critical care specialists to gauge the optimal duration of therapy in bacteraemic critically ill patients. Considerable variability existed amongst clinicians and undoubtedly refl ects the lack of robust data to guide best practice. However, length of treatment is only one aspect of optimising outcomes from antibiotic use. Future RCTs need to take into account whether adequate source control has been achieved, as this will bias duration of therapy. Moreover, it is clear that since many antibiotics deployed in critical care demonstrate time-dependent killing, inadequate doses are frequently used, which increases treatment failure and the emergence of resistance [3,4]. Pharmaco-kinetic optimisation that ensures adequate time above minimum inhibitory concentration should therefore be an integral component of any trial that compares dura-tion of antibiotic therapy [5].
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.744 | 0.654 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".