Analysis of the Time-To-Onset and Factors Affecting Clinical Outcomes of Immune Reconstitution Inflammatory Syndrome in People Living with HIV Using Data from the Japanese Spontaneous Reporting Database
Bibliographic record
Abstract
PURPOSE: Data on immune reconstitution inflammatory syndrome (IRIS), despite being a widely recognized complication of antiretroviral therapy, remain limited. The objective of the present study was to evaluate the time-to-onset and factors affecting clinical outcomes of IRIS in people living with HIV (PLWH) using data from the Japanese Adverse Drug Event Report (JADER) database. METHODS: Data of PLWH who developed IRIS as an adverse event were extracted from the JADER database. Cases with the data of both the start date of anti-HIV drug therapy and date of IRIS onset were included in the study. The survey items included sex, age, anti-HIV drug use, IRIS-compatible events, time-to-onset of IRIS, and clinical outcome. The time-to-onset of IRIS was evaluated in relation to anchor drug use. Overall, 79 cases were included in the analysis. RESULTS: The median (range) time-to-onset of IRIS was 29 (1-365) days, and it differed significantly between IRIS-compatible events (P = 0.029). In particular, the time-to-onset of Pneumocystis pneumonia-IRIS was the shortest among the IRIS-compatible events (median [range]: 12 [5-301] days). Age ≥ 50 years at IRIS onset appeared to be related to the poor clinical outcomes of IRIS in PLWH (P = 0.048). The use of integrase strand transfer inhibitors did not affect the time-to-onset of IRIS or clinical outcome of IRIS in PLWH. CONCLUSION: This analysis based on the data from the JADER database revealed that IRIS-compatible events were related to the time-to-onset of IRIS and that patients older than 50 years had poorer clinical outcomes of IRIS. This finding will be useful for healthcare professionals when considering medications for patients with HIV infection/AIDS and for the management of IRIS.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".