Identifying important risk factors for survival of HIV-infected patients using censored quantile regression
Bibliographic record
Abstract
Introduction: This study aimed to estimate the effect of potential risk factors on survival of human immunodeficiency virus/acquired immunodeficiency syndrome (AIDS) patients using censored quintile regression model. Material and methods:We used a dataset from a (registry-based) retrospective cohort study conducted in Tehran (from April, 2004 to March, 2018).Demographic information, such as age, sex, marital status, and educational level as well as behavioral information, including being-in-prison, drug/alcohol abuse and smoking, antiretroviral therapy, co-infection with tuberculosis (TB), and CD4+ cell count, were investigated as potential risk factors for AIDS progression.Censored quintile regression was used to estimate and investigate these factors for AIDS progression.Results: Mean age of patients was 33.93 years.Time to progression ranged from 0.01 to 223.17 months, and mean of time to progression was 40.55 months.A total of 1,249 (50.5%) patients experienced an event by end of the study.Impact of age, gender, prison, being addicted, being infected with tuberculosis, and using highly active antiretroviral therapy (HAART) were significant for most of quintiles (p < 0.05).Conclusions: It was shown that age, being prisoned, TB infection, and HAART were significantly associated with a lower time in AIDS progression.Censored quintile regression could be an appropriate choice for considering time-varying effects and easy interpretation of regression coefficients in analyzing AIDS progression data.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".