Factors influencing patient decision-making on a multimodal precision medicine algorithm for depression: a qualitative European multicentre study of the PROMPT consortium
Bibliographic record
Abstract
Background: Precision medicine promises to improve treatment outcomes by tailoring interventions to patients' individual characteristics. However, the use of precision medicine tools requires patient acceptance, which remains underexplored. This qualitative study investigated factors influencing patient perspectives on a multimodal precision medicine algorithm to predict antidepressant response in patients suffering from Major Depressive Disorder (MDD). Methods: To explore patients' perspectives on the use of a multimodal algorithm, semi-structured focus groups were conducted with 44 patients diagnosed with moderate to severe depression across three European sites (Germany, Poland, Italy) in the PROMPT study. Discussions were transcribed and translated into English for analysis. A qualitative, structured content analysis approach was then used to analyse the data. Results: Patients' perspectives on using a multimodal algorithm in MDD revealed a complex interplay of decision-making factors: while perceived clinical benefits, such as a reduction in trial-and-error prescribing and reassurance, promoted acceptance of the algorithm, concerns about cost, waiting time and the emotional impact of unfavourable results tended to discourage acceptance. Patients' general beliefs about mental illness and its treatment shaped their attitudes toward the application of the algorithm. Many participants emphasised the importance of trust in physicians and preferred testing within the context of an established therapeutic relationship. Misconceptions about the algorithm's accuracy and capabilities, and fears of medical reductionism, were common. Conclusions: While patients are open to the use of a multimodal precision medicine algorithm for MDD, they emphasised the need for individualised, transparent communication and emotional support. The results highlight the importance of patient-centred communication strategies and guidelines for the ethical implementation of precision psychiatry.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".