Toward Precision Psychiatry
Bibliographic record
Abstract
Major depressive disorder is a heterogeneous disorder affecting over 280 million people globally. Despite multiple treatment options, individual response to drugs varies significantly, and most patients go through a trial-and-error approach, resulting in multiple drug iterations before alleviation of symptoms is achieved. Treatment optimization is further complicated by lack of full elucidation of the neurobiology of depression. The high prevalence of nonresponse, coupled with the detrimental effects of prolonged disease on patient welfare, economic burden, and increased likelihood of recurrence, substantiates the critical need for robust tools capable of precisely matching patients with their most effective and safe treatment options in a time-sensitive manner. Research into technologies that tailor treatments to individual patients based on their unique molecular and cellular characteristics has led to the development of precision medicine tools ranging from pharmacogenetics through peripheral biomarkers and neuroimaging to a platform that uses patient-derived neurons as a substrate for in vitro patient-specific functional readouts. Novel precision medicine tools in depression are being introduced that aim to identify the optimal treatment for each patient. Such tools have the potential to significantly improve depression management by guiding treatment selection for prescribers and people with lived experience. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".