Clinical outcomes of administering an ePROM of barriers to adherence to ART to people with HIV through a patient portal
Bibliographic record
Abstract
Context: Adherence to antiretrovirals (ART) by people with HIV (PWH) is crucial, however, many face obstacles that go undiscussed with health professionals. We used the patient portal (Opal) to administer the I-Score, a 7-item electronic patient-reported outcome measure (ePROM) of barriers to ART adherence. Objective: To describe patient and service-related outcomes of the I-Score intervention and outline adherence barrier management by physicians. Study Design Analysis: 6-month one-arm implementation pilot study. Setting: A hospital-based clinic in Montreal, Canada. Population: Adult PWH on ART, speaking French or English, owning a smartphone, willing to use the patient portal, with a history of adherence issues. Intervention/Instrument: Patients completed the I-Score on the patient portal up to two days before visits with their physician at Baseline (T1), 3 months (T2), and 6 months (T3). We collected patients’ sociodemographic information at T1, and HIV viral loads at T1 and T3. At each visit, patients reported ART adherence, and physicians completed a checklist of actions undertaken based on I-Score results. Outcome Measures: Patient outcomes at T1 and T3, included HIV viral load undetectability, mean scores for self-reported adherence (score of 1 to 5) and adherence barriers (score of 1 to 10) over the past month. For service outcomes, we report frequencies and proportions of clinical visits where physicians took actions based on I-Score results. Results: Out of 26/32 participants who completed the intervention, 11/26 (42%) were female; 14/26 (54%) aged ≥ 50 years; and 8/26 (31%) had an income below $19,999. Most patients (23/26) had an undetectable viral load at T1 and T3. Concerning domains of adherence barriers, mean scores decreased for: Thoughts/Feelings (T1=2.9/10 to T3=2.3/10), Habits/Activities (1.8;1.7), Medication (2.1;1.5), and Health (2.0;1.3), but increased for: Social (2.5;2.9) and Economic Situation (2.4;2.6), and Care (1.3;1.4). Average self-reported adherence increased from T1 (4.11) to T3 (4.19). Physicians ordered new tests for 10/26 (38%) patients, changed the medication plan of 7/26 (27%) patients, and referred14/26 (54%) patients to a specialist. Conclusions: Administering the I-Score on a patient portal is feasible and appears to improve patient and service outcomes, yet with slight fluctuations. Further research is recommended for a more robust understanding of its efficacy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".