with a Decrease in the Monitored Viral Load
Bibliographic record
Abstract
Background: HIV drug resistance represents a major threat for effective treatment. We assessed the trends in the frequency of drug resistance mutations and the monitored viral load (VL) in treatment-naı̈ve (TN) and treatment-experienced (TE) individuals infected with HIV-1 in Québec, Canada, between 2001 and 2011. Methods and Findings: Resistance data were obtained from 4,105 and 5,086 genotypic tests performed on TN and TE patients, respectively. Concomitantly, 274,161 VL tests were carried out in the Province. Changes over time in drug resistance frequency and in different categories of VL were assessed using univariate logistic regression. Multiple logistic regression was used to evaluate associations between the rates of certain mutations and antiretroviral prescriptions. From 2001 to 2011, the proportion of undetectable VL test results continually increased, from 42.1 % to 75.9%, while a significant decrease in the frequency of resistance mutations associated with protease inhibitors [PI (from 54 % to 16%)], nucleoside [NRTI (from 78 % to 37%) and non-nucleoside reverse transcriptase inhibitors [NNRTI (from 44 % to 31%)] was observed in TE patients. In TN individuals, the overall frequency of transmitted drug resistance was 13.1%. A multiple logistic regression
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".