Understanding the impact of HIV among a vulnerable population in south India
Bibliographic record
Abstract
India has approximately 5.7 million people living with HIV/AIDS (PLWHA). Karnataka, a southern state in India containing the capital city of Bangalore, is a prime example of an area demonstrating high HIV prevalence. Snehadaan, a care and support centre for PLWHA in the outskirts of Bangalore, has requested assistance from partners at the Karnataka Health Promotion Trust and the University of Manitoba to understand better the needs of their patients to plan the expansion of their services. A retospective chart review of 210 charts from an adult population of HIV infected inpatients who died at Snehadaan between 2010 and 2011 was conducted to describe causes of mortality. Last admission details such as clinical presentation and tuberculosis (TB) and HIV diagnosis and treatment details were also explored. It was found that almost all the patients presented as WHO HIV stage III or IV (96.9%) and 76.4% had CD4 counts less than 200 cells/mm3, but only 56.7% were on antiretoviral therapy (ART) at time of admission. Furthermore, almost halt (49.6%) died within one year from their date of HIV diagnosis and 70.3% were diagnosed during their last admission with TB, a disease that accounted for 39.6% of the total deaths. These findings show that these patients presented late and at advanced stages in their disease and emphasize the importance of continuing to increase awareness of HIV for earlier testing and treatnent and of opportunistic infections such as TB, as they further compromise the health of an already immunocompromised population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".