Dimensional distribution of cortical abnormality across antipsychotics treatment-resistant and responsive schizophrenia
Bibliographic record
Abstract
BACKGROUND: One-third of patients with schizophrenia are treatment-resistant to non-clozapine antipsychotics (TRS), while the rest respond (NTRS). Examining whether TRS and NTRS represent different pathophysiologies is an important step toward precision medicine. METHODS: Focusing on cortical thickness (CT), we analyzed international multi-site cross-sectional datasets of magnetic resonance imaging comprising 110 patients with schizophrenia (NTRS = 46, TRS = 64) and 52 healthy controls (HCs). We utilized a logistic regression with L1-norm regularization to find brain regions related to either NTRS or TRS. We conducted nested 10-fold cross-validation and computed the accuracy and area under the curve (AUC). Then, we applied the NTRS classifier to patients with TRS, and vice versa. RESULTS: Patients with NTRS and TRS were classified from HCs with 65% and 78% accuracies and with the AUC of 0.69 and 0.85 (p = 0.014 and < 0.001, corrected), respectively. The left planum temporale (PT) and left anterior insula/inferior frontal gyrus (IFG) contributed to both NTRS and TRS classifiers. The left supramarginal gyrus only contributed to NTRS and right superior temporal sulcus and right lateral orbitofrontal cortex only to the TRS. The NTRS classifiers successfully distinguished those with TRS from HCs with the AUC of 0.78 (p < 0.001), while the TRS classifiers classified those with NTRS from HCs with the AUC of 0.69 (p = 0.015). CONCLUSION: Both NTRS and TRS could be distinguished from HCs on the basis of CT. The CT pathological basis of NTRS and TRS has commonalities, and TRS presents unique CT features.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".