Characteristics of hospital admissions for pneumonia in HIV-positive individuals in Winnipeg, Manitoba: a cross-sectional retrospective analysis
Bibliographic record
Abstract
Lung infection in human immunodeficiency virus (HIV)-positive individuals remains an important cause of morbidity and mortality, even in the current antiretroviral therapy era. Pneumonia is the most common cause of admission in HIV-positive individuals in our centre as reported in a previously published study. The objective of this retrospective observational study was to further characterize these admissions, with respect to index of disease severity at presentation, organisms identified, and investigations pursued including bronchoalveolar lavage (BAL). There were 123 unique patients accounting for a total of 209 admissions from 2005 to 2015. An organism was isolated in only 33% of all admissions (68/209). The most common organism was Pneumocystis jirovecii with a frequency of 29% of all admissions. Eighty-seven percent of presentations were mild, and 13% were moderate by CURB-65 criteria. A total of 39 BALs were performed, of which 27 yielded an organism (69%). Considering the burden of disease, low diagnostic yield of the current diagnostic strategy and increased morbidity and mortality caused by pneumonia in HIV-positive individuals, further methods are needed to more accurately target therapy. The preponderance of mild disease in this study suggests that better diagnostic tests may identify individuals that can be candidates for outpatient therapy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".