Bibliographic record
Abstract
After a 12-year search for the antipsychotic receptor, the binding site was discovered and labelled by [3H]haloperidol in 1975. Of the various neurotransmitters, dopamine was the most potent in inhibiting the binding of [3H]haloperidol, indicating that the antipsychotic receptor was a dopamine receptor, now named the dopamine D2 receptor, a major targeting site in schizophrenia. All antipsychotic drugs, including traditional and newer antipsychotics, either bind to D2 in direct relation to their clinical potencies or hinder normal dopamine neurotransmission, as in the case of partial dopamine agonists. In fact, the antipsychotic concentrations found in the plasma water of treated patients closely match the predicted therapeutic absolute concentrations, adjusted for the 60-75% D2 occupancy needed for clinical efficacy. Antipsychotics that elicit low or no Parkinsonism or prolactinaemia are loosely attached to D2 and rapidly dissociate from D2, whereas those eliciting Parkinsonism stay tightly attached to D2 for many hours. Because animal models of psychosis (amfetamine sensitisation, brain lesions) all show a marked elevation in the number of high-affinity states of D2, the antipsychotics are thought to specifically target these D2High states in psychosis in general and schizophrenia in particular.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".