Structured Online Support to Inform and Assist Antidepressant Deprescribing in Primary Care: Protocol for a Pragmatic, Randomized Controlled Trial (The WiserAD Trial) (Preprint)
Bibliographic record
Abstract
BACKGROUND: The use of antidepressant medications is increasing globally. Despite their obvious benefits, ongoing use of these medications is often not properly monitored or deprescribed when a person returns to better mental health; in addition, the proportion of prescriptions provided to those who do not suffer from clinical depression leads to a personal and societal cost burden. OBJECTIVE: This trial aims to assess the clinical and cost effectiveness of an online support tool to help patients with mild to no symptoms of depression and their general practitioners to manage the careful and appropriate tapering and cessation of antidepressants at 6 months compared to attention control. METHODS: This stratified, single-blind, parallel, two-arm, superiority randomised controlled trial in Australian primary care of individuals with mild to no symptoms of depression aged 18 to 75 years old and have been taking antidepressant medication for longer than 12 months After informed consent, 340 eligible patients will be randomised 1:1, stratified by general practice or state of residence if recruited via social media, into the active intervention arm, where they will be asked to reduce their antidepressant with the aid of a clinically guided online support tool, or the attention control arm, that will continue with usual care. Participants in both arms will be provided with information about antidepressants through the Beyond Blue website and followed up at 3, 6 ,12 and 18 months to record antidepressant use, depression and anxiety symptom severity, quality of life and health economics information. Intention to treat analysis will determine the clinical effectiveness of the online tool compared to attention control, where the primary outcome is between-arm difference in the proportion of participants with successful cessation of medication at 6 months with depression remaining mild or absent. Cost-consequence and cost-utility analyses will be used to determine the cost effectiveness of the intervention and its impact on quality of life, compared to control. RESULTS: At submission of this manuscript in July 2025, 310 participants had been randomized and recruitment was ongoing. The target number of 340 randomized participants was achieved in January 2026. CONCLUSIONS: The WiserAD online support tool assists patients and their GP with deprescribing and may lead to successful cessation of antidepressant medication, resulting in an enhanced quality of life and cost savings over the longer term. CLINICALTRIAL: ANZCTR ACTRN12622000567729, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=ACTRN12622000567729; ISRCTN 11562922, https://www.isrctn.com/ISRCTN11562922; ClinicalTrials.gov NCT05355025, https://clinicaltrials.gov/study/NCT05355025. INTERNATIONAL REGISTERED REPORT: DERR1-10.2196/81858.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.017 |
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".