Sending repeat cultures: is there a role in the management of bacteremic episodes? (SCRIBE study)
Bibliographic record
Abstract
BACKGROUND: In the management of bacteremia, positive repeat blood cultures (persistent bacteremia) are associated with increased mortality. However, blood cultures are costly and it is likely unnecessary to repeat them for many patients. We assessed predictors of persistent bacteremia that should prompt repeat blood cultures. METHODS: We conducted a retrospective cohort study of bacteremias at an academic hospital from April 2010 to June 2014. We examined variables associated with patients undergoing repeat blood cultures, and with repeat cultures being positive. A nested case control analysis was performed on a subset of patients with repeat cultures. RESULTS: Among 1801 index bacteremias, repeat cultures were drawn for 701 patients (38.9 %), and 118 persistent bacteremias (6.6 %) were detected. Endovascular source (adjusted odds ratio [aOR], 7.66; 95 % confidence interval [CI], 2.30-25.48), epidural source (aOR, 26.99; 95 % CI, 1.91-391.08), and Staphylococcus aureus bacteremia (aOR, 4.49; 95 % CI, 1.88-10.73) were independently associated with persistent bacteremia. Escherichia coli (5.1 %, P = 0.006), viridans group (1.7 %, P = 0.035) and β-hemolytic streptococci (0 %, P = 0.028) were associated with a lower likelihood of persistent bacteremia. Patients with persistent bacteremia were less likely to have achieved source control within 48 h of the index event (29.7 % vs 52.5 %, P < .001), but after variable reduction, source control was not retained in the final multivariable model. CONCLUSIONS: Patients with S. aureus bacteremia or endovascular infection are at risk of persistent bacteremia. Achieving source control within 48 h of the index bacteremia may help clear the infection. Repeat cultures after 48 h are low yield for most Gram-negative and streptococcal bacteremias.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".