Clinical and economic burden of infections in hospitalized solid organ transplant recipients compared with the general population in Canada – a retrospective cohort study
Bibliographic record
Abstract
Infections continue to be a major cause of post-transplant morbidity and mortality, requiring increased health services utilization. Estimates on the magnitude of this impact are relatively unknown. Using national administrative databases, we compared mortality, acute care health services utilization, and costs in solid organ transplant (SOT) recipients to nontransplant patients using a retrospective cohort of hospitalizations in Canada (excluding Manitoba/Quebec) between April-2009 and March-2014, with a diagnosis of pneumonia, urinary tract infection (UTI), or sepsis. Costs were analyzed using multivariable linear regression. We examined 816 324 admissions in total: 408 352 pneumonia; 328 066 UTI's; and 128 275 sepsis. Unadjusted mean costs were greater in SOT compared to non-SOT patients with pneumonia [(C$14 923 ± C$29 147) vs. (C$11 274 ± C$18 284)] and sepsis [(C$23 434 ± C$39 685) vs. (C$20 849 ± C$36 257)]. Mortality (7.6% vs. 12.5%; P < 0.001), long-term care transfer (5.3% vs. 16.5%; P < 0.001), and mean length of stay (11.0 ± 17.7 days vs. 13.1 ± 24.9 days; P < 0.001) were lower in SOT. More SOT patients could be discharged home (63.2% vs. 44.3%; P < 0.001), but required more specialized care (23.5% vs. 16.1%; P < 0.001). Adjusting for age and comorbidities, hospitalization costs for SOT patients were 10% (95% CI: 8-12%) lower compared to non-SOT patients. Increased absolute hospitalization costs for these infections are tempered by lower adjusted costs and favorable clinical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".