HIV antiviral drug resistance: patient comprehension
Bibliographic record
Abstract
A patient's understanding and use of healthcare information can affect their decisions regarding treatment. Better patient understanding about HIV resistance may improve adherence to therapy, decrease population viral load and extend the use of first-line HIV therapies. We examined knowledge of developing HIV resistance and explored treatment outcomes in a cohort of HIV+ persons on highly active antiretroviral therapy (HAART). The longitudinal investigations into supportive and ancillary health services (LISA) cohort is a prospective study of HIV+ persons on HAART. A comprehensive interviewer-administrated survey collected socio-demographic variables. Drug resistance knowledge was determined using a three-part definition. Clinical markers were collected through linkage with the Drug Treatment Program (DTP) at the British Columbia Centre for Excellence in HIV/AIDS. Categorical variables were compared using Fisher's Exact Test and continuous variables using the Wilcoxon rank-sum test. Proportional odds logistic regression was performed for the adjusted multivariable analysis. Of 457 LISA participants, less than 4% completely defined HIV resistance and 20% reported that they had not discussed resistance with their physician. Overall, 61% of the cohort is >or=95% adherent based on prescription refills. Owing to small numbers pooling was preformed for analyses. The model showed that being younger (OR=0.97, 95% CI: 0.95-0.99), having greater than high school education (OR=1.64, 95% CI: 1.07-2.51), discussing medication with physicians (OR=3.67, 95% CI: 1.76-7.64), having high provider trust (OR=1.02, 95% CI: 1.01-1.03), and receiving one-to-one counseling by a pharmacist (OR=2.14, 95% CI: 1.41-3.24) are predictive of a complete or partial definition of HIV resistance. The probability of completely defining HIV resistance increased from 15.8 to 63.9% if respondents had discussed HIV medication with both a physician and a pharmacist. Although the understanding of HIV resistance showed no differences in treatment outcomes in this cohort, overall adherence and complete understanding of HIV resistance were low. If patient understanding could be improved through discussions with physicians and pharmacists, potential exists to enhance overall adherence and treatment outcomes.
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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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".