Positron Emission Tomography in<i>Staphylococcus aureus</i>Bacteremia: Peeking Under the Covers
Bibliographic record
Abstract
(See the Major Article by van der Vaart et al. on pages 9–15.) Despite improvements over the past 20 years [1], the morbidity and mortality associated with Staphylococcus aureus bacteremia are substantial with up to a 20% mortality rate at 30 days. S. aureus has a predilection toward deep-seated infections, which are not always clinically apparent, thus contributing to inadequate treatment or recurrence. The complex nature of S. aureus bacteremia has benefitted from interventions to standardize processes of care, such as automatic consultation with an infectious diseases specialist [2] and the implementation of care bundles [3]. In theory, improved outcomes have been mediated through guidance related to appropriate antibiotic therapy (eg, class, dose, optimal pharmacokinetics, route, and duration) and recommendations to obtain and operationalize source control (eg, line removal or drainage of abscesses). It follows that better identification of clinically silent foci of infection leading to subsequent refinements in management could further reduce mortality rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.020 |
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".