A nosocomial sapovirus-associated outbreak of gastroenteritis in adults
Bibliographic record
Abstract
Greenland is a high-income country with free access to human immunodeficiency virus (HIV) care, including highly active antiretroviral therapy (HAART). We aimed to examine the HIV prevalence, age and gender distribution, and the effectiveness of HAART on CD4 positive cell count, viral load (VL), and mortality in the Greenland HIV-infected population. In this population-based cohort study we collected demographic, clinical and biochemical data on all HIV-infected patients followed in health clinics since 1995. For each case, we identified 100 age- and gender-matched population controls. The HIV cohort included 103 patients of whom 91% were Inuit; 81% were infected heterosexually. Only 40% of the patients had a VL <400 copies/ml at 48 weeks after starting HAART, and patients on HAART had a substantial excess relative mortality compared with the general population (mortality rate ratio [MRR]: 10.6; 95% confidence interval [CI]: 6.9-16.4). After the introduction of HAART, the mortality decreased (MRR: 2.5; 95% CI 0.9-6.8), but remained high with a mortality rate of 62 per 1000 person-years (py) (95% CI 29-129). Our findings underline the difficulties of implementing successful HIV treatment even with unlimited economic resources and free access to health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".