Summary Guide to Psychotropic Medication and Treatment
Bibliographic record
Abstract
T his is a sketch of psychotropic drugs used in treating or managing psychotic depression. We list the generic name of the drug first. Using only the generic name directs focus to the patient and away from drug advertising. It prevents the costly suggestion associated with mentioning the brand name. Some drugs identified below as costly should soon cost less, as generics become available. Antipsychotic tranquilizers The drug type “antipsychotic” is a misnomer. All antipsychotics powerfully affect thought processes in everyone who takes them. They do not make thoughts normal and they are not specifically antipsychotic. Rather, they generally decrease the complexity and amount of thinking. Most patients discontinue their antipsychotic drug, probably because it decreases feelings of pleasure in life experience and reward in accomplishment overall, including for taking medication properly. Other side effects vary; for example, some antipsychotics cause grogginess but others do not. Drugs that are still on patent typically cost several times as much as generics. The original antipsychotics were called neuroleptics (the commonest class of which were phenothiazines). These drugs block the effects of the neurotransmitter dopamine, causing dopamine deficiency. Dopamine blockade decreases outflow of nerve cell activity from the prefrontal lobes of the brain, analogous to a faucet decreasing water flow. The prefrontal lobes house the complex thinking of the human brain. Dopamine deficiency is part of Parkinson's disease.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.207 | 0.186 |
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".