Adolescent Medicine Trials Network for HIV/AIDS Interventions Data Harmonization: Rationale and Development of Guidelines
Bibliographic record
Abstract
BACKGROUND: The Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program aims to defeat the rising HIV epidemic among adolescents and young adults in the United States. OBJECTIVE: This study aims to optimize cross-study analyses and comparisons of standardized measures (variables) collected in the ATN. METHODS: Guidelines were developed for harmonizing measures to be collected across ATN studies. RESULTS: Eight domains were identified for harmonization-Demographics and Socioeconomic Characteristics, Sexual Behavior and Risk, Substance Use and Abuse, HIV-Positive Cascade, HIV-Negative Cascade, Mental Health, Social Support and Isolation, and Pre-exposure Prophylaxis Cascade. CONCLUSIONS: The collection of selected key measures in a uniform manner across studies facilitates the characterization of participant populations, comparisons between studies, and pooled analysis of data from multiple studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.714 | 0.753 |
| Meta-epidemiology (narrow) | 0.004 | 0.009 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.022 | 0.031 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.024 | 0.021 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".