Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l’outil PETRUSHKA
Bibliographic record
Abstract
OBJECTIVE: We summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part of the PETRUSHKA trial (Personalize antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs, and big datA). METHODS: The PETRUSHKA tool: (a) is based on prediction models, which use a combination of advanced analytics, i.e., traditional statistics, and machine learning methods; (b) utilizes electronic health records from primary care patients with depressive disorder in England and data from randomized controlled trials on antidepressants in depression, both at aggregate and individual patient level; (c) incorporates preferences from patients and clinicians (especially about adverse events); (d) generates a ranked list of personalized treatment recommendations to inform the discussion between clinicians and patients, and facilitates the final treatment choice. The PETRUSHKA tool is implemented as a web-based application, accessible from any computer, smartphone or tablet. RESULTS: We employed a bespoke algorithm to identify the best antidepressant for each individual patient, using patients' clinical and demographic characteristics and harnessing the power of innovations in digital technology, large datasets and machine learning. We established a dedicated group of patient representatives that were involved in the co-production of the tool, to maximize its impact in real-world clinical practice across the world. To test the tool, we designed an international multi-site, randomized trial (target sample: 504 participants), comparing the PETRUSHKA tool with usual care to personalize pharmacological treatment in patients with depressive disorder across Brazil, Canada and the UK. CONCLUSIONS: Using evidence-based patient decision aids has been recommended to support shared decision-making when quality is assured. Future studies in precision mental health should develop multimodal web tools, incorporating patients' preferences and their individual demographic, cultural, clinical, and genetic characteristics.Plain Language Summary TitleTailoring antidepressant treatment to individual patients with depression: the PETRUSHKA tool.
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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 | 0.016 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".