Infectious diseases consultation improves key performance metrics in the management of <i>Staphylococcus aureus</i> bacteremia: A multicentre cohort study
Bibliographic record
Abstract
Background: bacteremia (SAB) is associated with significant morbidity and mortality. We sought to identify factors associated with infectious diseases consultation (IDC) and understand how IDC associates with SAB patient management and outcomes. Methods: A multicentre retrospective study was performed between 2012 and 2014 in a large Canadian Health Zone in order to determine factors associated with IDC and performance of key quality of care determinants in SAB management and clinical outcomes. Factors subject to quality of care determinants were established a priori and studied for associations with IDC and 30-day all-cause mortality using multivariable analysis. Results: Of 961 SAB episodes experienced by 892 adult patients, 605 episodes received an IDC. Patients receiving IDC were more likely to have prosthetic valves and joints and to have community-acquired and known sources of SAB, but increasing age decreased IDC occurrence. IDC was the strongest independent predictor for quality of care performance metrics, including repeat blood cultures and echocardiography. Mortality at 30 days was 20% in the cohort, and protective factors included IDC, achievement of source control, targeted therapy within 48 hours, and follow-up blood cultures but not the performance of echocardiography. Conclusions: There were significant gaps between the treatments and investigations that patients actually received for SAB and what is considered the optimal management of their condition. IDC is associated with improved attainment of targeted SAB quality of care determinants and reduced mortality rates. Based on our findings, we propose a policy of mandatory IDC for all cases of SAB to improve patient management and outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".