Determining the Feasibility of a Mail-In Saliva Sample Protocol for Persons Living with HIV in Two Urban Northeast United States Clinics
Bibliographic record
Abstract
Matthew D Mara,1,2 Cynthia Miguel,3 Nina H Lin,4 Curt G Beckwith,5 Michelle Henshaw2,6 1General Dentistry, Boston University Henry M. Goldman School of Dental Medicine, Boston, MA, USA; 2Office of Global and Population Health, Boston, Massachusetts, University Henry M. Goldman School of Dental Medicine, Boston, MA, USA; 3Social Determinants of Health Analytics, Highmark Health, Pittsburgh, PA, USA; 4Division of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; 5Division of Infectious Diseases, Department of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA; 6Health Policy and Health Services Research, Boston University Henry M. Goldman School of Dental Medicine, Boston, MA, USACorrespondence: Matthew D Mara, General Dentistry, Boston University, 560 Harrison Ave, Third Floor, Boston, MA, 02118, USA, Email maramb@bu.eduAbstarct: Background: Mail-in saliva samples are a participant-centered means of monitoring patients’ oral and overall health that may help to reduce unnecessary medical trips and more efficiently use provider time. The use and acceptability of mail-in saliva samples have been studied in high-risk populations; however, it has not been studied in cohorts of people living with HIV (PLWH).Objective: To investigate the feasibility of PLWHs’ willingness to provide mail-in saliva samples, 900 saliva collection kits were mailed to a randomized sample of PLWH across two urban and diverse clinics in the Northeast United States in 2019– 2020.Results: 73/900 (8%) packages were returned to study personnel, with 100% (73/73) containing saliva deemed usable for analysis. 55/900 (6%) of packages were returned to the study team undeliverable. Further information may be gained from investigating the influence of personalized test results, health literacy, the requirement of the oral health survey, and study compensation that may affect an individual’s motivation to participate in the study.Conclusion: While findings suggest limited feasibility of mail-in saliva collection without personalized test results in a cohort of PLWH in terms of response rate, all returned saliva samples were usable for analysis suggesting that addressing potential barriers may improve response rates and the feasibility of using mail-in salivary diagnostics to monitor disease progression in PLWH in future studies.Keywords: respondent driven sampling, adherence interventions, saliva diagnostics, remote collection, disease monitoring
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".