An Internet-based Controlled Trial Aimed to Improve Osteoporosis Prevention among Chronic Glucocorticoid Users
Bibliographic record
Abstract
OBJECTIVE: To address the low prevention and treatment rates for those at risk of glucocorticoid-induced osteoporosis (GIOP), we evaluated the influence of a direct-to-patient, Internet-based educational video intervention using "storytelling" on rates of antiosteoporosis medication use among chronic glucocorticoid users who were members of an online pharmacy refill service. METHODS: We identified members who refilled ≥ 5 mg/day of prednisone (or equivalent) for 90 contiguous days and had no GIOP therapy for ≥ 12 months. Using patient stories, we developed an online video addressing risk factors and treatment options, and delivered it to members refilling a glucocorticoid prescription. The intervention consisted of two 45-day "Video ON" periods, during which the video automatically appeared at the time of refill, and two 45-day "Video OFF" periods, during which there was no video. Members could also "self-initiate" watching the video by going to the video link. We used an interrupted time series design to evaluate the effectiveness of this intervention on GIOP prescription therapies over 6 months. RESULTS: Among 3017 members (64.8%) exposed to the intervention, 59% had measurable video viewing time, of which 3% "self-initiated" the video. The GIOP prescription rate in the "Video ON" group was 2.9% versus 2.7% for the "Video OFF" group. There was a nonsignificant trend toward greater GIOP prescription in members who self-initiated the video versus automated viewing (5.7% vs 2.9%, p = 0.1). CONCLUSION: Among adults at high risk of GIOP, prescription rates were not significantly affected by an online educational video presented at the time of glucocorticoid refill. ClinicalTrials.gov Identifier: NCT01378689.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".