Invasive Pneumococcal Disease in Solid Organ Transplant Recipients—10-Year Prospective Population Surveillance
Bibliographic record
Abstract
Prospective population-based surveillance to assess the incidence and impact of invasive pneumococcal disease (IPD) in organ transplant patients is lacking. By using a population-based Invasive Bacterial Diseases Network surveillance program, we studied the incidence, clinical significance, serotypes and antimicrobial resistance pattern of IPD in a large cohort of adult transplant patients and the general population. Streptococcus pneumoniae isolates and patient data were collected prospectively from 1995 to 2004. We identified 21 cases of IPD (based on sterile-site isolates) in our organ transplant population over a 10-year period. This translated to an incidence rate of 146 infections per 100,000 persons per year. This compared to an incidence of 11.5 per 100,000 persons per year in the general population (R(R)=12.8; 95% CI 8.1-19.9, p<0.00001). If nonsterile-site isolates (respiratory tract) were included, the incidence rate in transplant patients was 419 of 100 000 persons per year. Serotypes 23F and 22F were most common, and 85.0% had a serotype included in the 23-valent pneumococcal vaccine. The antimicrobial resistance rates were high, especially for penicillin and trimethoprim-sulfamethoxazole (TMP/SMX), but were not significantly different from the general population. Solid organ transplant recipients are at significantly greater risk for IPD than the general population. Preventative strategies are necessary.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".