Guidelines are needed for studies of pre-treatment HIV drug resistance: a methodological study
Bibliographic record
Abstract
BACKGROUND: The expansion of access to antiretroviral therapy (ART) has been accompanied by an increase in pre-treatment drug resistance (PDR). While it is critical to monitor the increasing prevalence of PDR across countries and populations to inform optimal regimen selection, the completeness of reporting is often suboptimal, limiting the interpretation and generalizability of the results. Indeed, there is no formal guidance on how studies investigating the prevalence of drug resistance should be reported. Thus, we sought to determine the completeness of reporting in studies of PDR and the factors associated with sub-optimal reporting to ascertain the need for guidelines. METHODS: As part of a systematic review on the global prevalence of PDR in key populations (men who have sex with men, sex workers, transgender people, people who inject drugs and people in prisons), we searched 10 electronic databases until January 2019. We extracted information on selected study characteristics useful for interpreting prevalence data. Data were extracted in duplicate. Analyses of variance and correlation were used to explore factors that may explain the number of items reported. RESULTS: We found 650 studies of which 387 were screened as full text and 234 were deemed eligible. The included studies were published between 1997 and 2019 and included a median of 239 (quartile 1 = 101; quartile 3 = 778) participants. Most studies originated from high-income countries (125/234; 53.0%). Of 23 relevant data items, including study design, setting, participant sociodemographic characteristics, HIV risk factors, type of resistance test conducted, definition of resistance, the mean (standard deviation) number of items reported was 13 (2.2). We found that more items were reported in studies published more recently (r = 0.20; p < 0.002) and in studies at low risk of bias (F [2231] = 8.142; p < 0.001). CONCLUSIONS: Incomplete reporting in studies on PDR makes characterising levels of PDR in subpopulations across countries challenging. Hence, guidelines are needed to define a minimum set of variables to be included in such studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.746 | 0.871 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.027 | 0.035 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".