Can a clinical prediction tool guide HIV-testing decisions? Experience at a national hospital in Guatemala
Bibliographic record
Abstract
The USA and international recommendations no longer emphasize using risk factors to target groups for HIV-testing. Using a Guatemalan database of HIV tests, we developed a clinical prediction rule to guide decisions on HIV-testing. Prior to HIV-testing, data were collected on demographics, risk factors and prior testing. Based on a theoretical construct incorporating demographics, known HIV risk factors and symptoms, we developed a logistic regression model to predict HIV seropositivity. Between 2000 and 2005, 16,471 tests were performed, of which 19.8% were positive. The algorithm successfully predicted 1883 of 2489 HIV-positive tests (sensitivity 76%, likelihood ratio [LR]-positive 2.45) and 6282 of 9086 HIV-negative tests (specificity 69%, LR-negative 0.35). Although the model indices are robust, applying the model in a clinical setting would have little impact on improving selective testing practices. Our findings support current recommendations for universal HIV-testing, not selective testing based on risk factors. Before these recommendations can be adopted widely in Guatemala, treatment access needs to be assured and protections put in place for people diagnosed with HIV infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".