Pharmacogenetics for off-patent antipsychotics: reframing the risk for tardive dyskinesia and access to essential medicines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
First-generation antipsychotics (FGAs) induce tardive dyskinesia, a debilitating involuntary hyperkinetic movement disorder, in 20-50% of individuals with a psychotic illness during chronic treatment. There is presently no curative treatment or definitive predictive test for tardive dyskinesia. The authors note that the three antipsychotic drugs enlisted in the most recent (14th) World Health Organization Model List of Essential Medicines--chlorpromazine, fluphenazine and haloperidol--belong to the FGA therapeutic class. In this regard, the need to choose between the competing objectives of ensuring global access to affordable and efficacious medicines, such as FGAs, and the formidable long-term risk for tardive dyskinesia, may create an ethical conundrum. Pharmacogenetics has thus far been conceptually framed as a tool to individualize therapy with new drugs under patent protection. However, the authors suggest that pharmacogenetics may also improve access to pharmacotherapy through the reintroduction of affordable second-line generic drugs or FGAs with suboptimal safety, as first-line therapy, in targeted subpopulations in whom they present a lower risk for tardive dyskinesia. To impact positively on global public health and distributive justice, a directory complementary to the essential medicines library--one that enlists the 'essential biomarkers' required for optimal pharmacotherapy--may benefit patients who do not have adequate access to new antipsychotic medications. This review discusses pharmacogenetic associations of tardive dyskinesia that are in part supported by meta-analyses and the oxidative stress-neuronal degeneration hypothesis.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it