Late diagnosis of HIV infection and its associated factors in Shiraz, Southern Iran: a retrospective study
Bibliographic record
Abstract
Late diagnosis (LD) of HIV infection can give rise to suboptimal responses to antiretroviral treatment. The current study aimed to determine the prevalence and associated factors of HIV LD in Shiraz, Southern Iran. This retrospective cohort study was conducted from August 1997 to May 2018. Medical records were examined to extract required data. Individuals with time period less than three months from HIV diagnosis to an advanced phase of AIDS on CD4 < 350 were considered as LD. Multivariable logistic regression used to investigate the associated factors of late HIV diagnosis and adjusted odds ratios were reported. Of 1385 individuals, 1043 (75.3%) were considered as LD. The following factors were identified as the associated factors of LD: age at diagnosis (OR = 1.72, 95% CI: 1.22, 2.41), HCV co-infection (OR = 1.65, 95% CI: 1.23, 2.21), not living in Shiraz (OR = 1.36, 95% CI: 1.02, 1.82), increased likelihood of LD and being housewife (OR = 0.67, 95% CI: 0.47, 0.95) which decreased the likelihood of LD. Our results showed delayed diagnosis of a large proportion of individuals with HIV. It is critical to improve the HIV testing guidelines in Iran to identify individuals with HIV without delays in order to provide them with timely HIV medical care and treatment.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".