The Co-Seasonality of Pneumonia and Influenza With Clostridium difficile Infection in the United States, 1993-2008
Bibliographic record
Abstract
Seasonal variations in the incidence of pneumonia and influenza are associated with nosocomial Clostridium difficile infection (CDI) incidence, but the reasons why remain unclear. Our objective was to consider the impact of pneumonia and influenza timing and severity on CDI incidence. We conducted a retrospective cohort study using the US National Hospital Discharge Survey sample. Hospitalized patients with a diagnosis of CDI or pneumonia and influenza between 1993 and 2008 were identified from the National Hospital Discharge Survey data set. Poisson regression models of monthly CDI incidence were used to measure 1) the time lag between the annual pneumonia and influenza prevalence peak and the annual CDI incidence peak and 2) the lagged effect of pneumonia and influenza prevalence on CDI incidence. CDI was identified in 18,465 discharges (8.52 per 1,000 discharges). Peak pneumonia prevalence preceded peak CDI incidence by 9.14 weeks (95% confidence interval: 4.61, 13.67). A 1% increase in pneumonia prevalence was associated with a cumulative effect of 11.3% over a 6-month lag period (relative risk = 1.113, 95% confidence interval: 1.073, 1.153). Future research could seek to understand which mediating pathways, including changes in broad-spectrum antibiotic prescribing and hospital crowding, are most responsible for the associated changes in incidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".