Dengue among immunocompromised patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although there is a rising trend in both dengue cases and immunocompromised conditions, there is limited research on how common severe dengue is in immunocompromised individuals. This data is key for those advising the ever-increasing numbers of immunocompromised travellers. METHODS: We conducted a systematic review and meta-analysis of studies reporting dengue frequency or outcomes in immunocompromised populations. Non-human and review articles were excluded. Risk of bias was assessed using the ROBINS-E tool. RESULTS: Eighty-five studies were included; 63 had a very high risk of bias. Frequency of dengue among different immunocompromised patient cohorts varied from 0.3% to 6.3%. Of 1182 dengue cases, 664 had autoimmune diseases, 388 were post-solid organ transplant (SOT), 20 post-stem cell transplant (HSCT), 28 had haematological malignancies, 24 non-haematological malignancies and 58 were HIV-positive. Severe dengue and mortality were estimated at 0.27 [0.22-0.33] and 0.14 [0.10-0.18], decreasing to 0.16 [0.09-0.27] and 0.04 [0.03-0.05] when very high risk or small-sample studies were excluded. Twenty-three (5.6%) of post-transplant dengue patients were considered as donor-related. Mortality reached 66.7% in HSCT and 10% in SOT. Dengue RNA was detectable up to four months in blood and up to two years in urine; viable virus was isolated from urine at nine months. CONCLUSIONS: Dengue in immunocompromised, especially HSCT, is associated with high severity and mortality. It also has the potential for prolonged viral persistence.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".