The search for new off-label indications for antidepressant, antianxiety, antipsychotic and anticonvulsant drugs.
Bibliographic record
Abstract
Most drugs are prescribed for several illnesses, but it took several years for psychotropic drugs to have multiple clinical indications. Our search for serotonergic drugs in affective illnesses and related disorders led to new off-label indications for fluoxetine, sertraline, tryptophan, clonazepam, alprazolam, tomoxetine, buproprion, duloxetine, risperidone and gabapentin. Various clinical trial designs were used for these proof-of-concept studies. Novel therapeutic uses of benzodiazepines, such as in panic disorder and mania, were found with the introduction of 2 high-potency benzodiazepines, clonazepam and alprazolam, which were thought to have serotonergic properties. Our initial clinical trials of fluoxetine and sertraline led to their approved indications in the treatment of obsessive-compulsive disorder, and our trials of gabapentin led to new indications in anxiety disorders (generalized anxiety, panic attack and social phobia) and sleep disorders (insomnia).
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 | 0.001 | 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".