Cohort Profile: Seek and Treat for the Optimal Prevention of HIV/AIDS in British Columbia (STOP HIV/AIDS BC)
Bibliographic record
Abstract
The Seek and Treat for Optimal Prevention of HIV/AIDS (STOP HIV/AIDS) cohort is a census of all identified HIV-positive individuals in the province of British Columbia. It was formed through the linkage of nine provincial treatment, surveillance and administrative databases. This open cohort allows for bidirectional analyses from 1996 onward and is refreshed annually. Extensive data collection for cohort members includes demographic information, detailed clinical and laboratory data, complete prescription drug use including antiretroviral agents, and information on health service utilization encompassing inpatient and outpatient care, addictions treatment and palliative care. This cohort provides an unprecedented opportunity to evaluate, over an extended time period, patterns and determinants of key outcomes including engagement in the cascade of HIV care from diagnosis to treatment to viral suppression as well as monitoring trends in medical costs, health outcomes and other key healthcare delivery indicators at a population level with wide-ranging, high-quality data. The overall purpose of these activities is to enable the development and implementation of strategically targeted interventions to improve access to testing, care and treatment for all HIV-positive individuals living in British Columbia. As a programme of the Ministry of Health, the STOP HIV/AIDS Evaluation Evaluation Team welcomes input from all stakeholders and possible partners via contact with the Director of Operations for the British Columbia Centre for excellence in HIV AIDS, Ms. Irene Day (iday@cfenet.ubc.ca).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".