Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort
Bibliographic record
Abstract
Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".