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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".