Associations of pathogen‐specific and host‐specific characteristics with disease outcome in patients with <i>Staphylococcus aureus</i> bacteremic pneumonia
Bibliographic record
Abstract
OBJECTIVE: (SA) bacteremic pneumonia (SABP) outcome with patient-specific and SA-specific variables. METHODS: AT production, haemolytic activity and expression of ClfA and ClfB. Controls were sera from gram-negative bacteremia patients with or without pneumonia and uninfected subjects. Levels of IgGs, IgMs and neutralizing antibodies (NAbs) against SA antigens were quantified and analysed by one-way ANOVA. Associations of patient outcomes with patient variables, antibody levels and isolate characteristics were evaluated by univariate and multivariate logistic regression analyses. RESULTS: SABP patients had higher levels of IgGs against eight virulence factors and anti-alpha toxin (AT) NAbs than uninfected controls. Levels of IgG against AT and IgMs against ClfA, FnbpA and SdrC were higher in clinically cured SABP patients than in clinical failures. Anti-LukAB NAb levels were elevated in all cohorts. Increased odds of cure correlated with higher haemolytic activity of SA strains, longer time between surgery and bacteremia (> 30 days), longer duration of antibiotic therapy, lower acute physiology and total APACHE II scores, lack of persistent fever for > 72 h and higher levels of antibodies against AT (IgG), ClfA (IgM), FnbpA (IgM) and SdrC (IgM). DISCUSSION: Limitations included the cross-sectional observational nature of the study, small sample size and inability to measure antibody levels against all SA virulence factors. CONCLUSION: Our results suggest that SABP patients may benefit from immunotherapy targeting multiple SA antigens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".