Supportive text messages for patients with alcohol use disorder and a comorbid depression: a protocol for a single-blind randomised controlled aftercare trial
Bibliographic record
Abstract
INTRODUCTION: Alcohol use disorders (AUDs) and mood disorders commonly co-occur, and are associated with a range of negative outcomes for patients. Mobile phone technology has the potential to provide personalised support for such patients and potentially improve outcomes in this difficult-to-treat cohort. The aim of this study is to examine whether receiving supporting SMS text messages, following discharge from an inpatient dual diagnosis treatment programme, has a positive impact on mood and alcohol abstinence in patients with an AUD and a comorbid mood disorder. METHODS AND ANALYSIS: The present study is a single-blind randomised controlled trial. Patients aged 18-70 years who meet the criteria for both alcohol dependency syndrome/alcohol abuse and either major depressive disorder or bipolar disorder according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders IV Axis I will be randomised to receive twice-daily supportive SMS text messages for 6 months plus treatment as usual, or treatment as usual alone, and will be followed-up at 3, 6, 9 and 12 months postdischarge. Primary outcome measures will include changes from baseline in cumulative abstinence duration, which will be expressed as the proportion of days abstinent from alcohol in the preceding 90 days, and changes from baseline in Beck Depression Inventory scores. ETHICS AND DISSEMINATION: The trial has received full ethical approval from the St. Patrick's Hospital Research Ethics Committee (protocol 13/14). Results of the trial will be disseminated through peer-reviewed journal articles and at academic conferences. TRIAL REGISTRATION NUMBER: NCT02404662; Pre-results.
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".