Treatment Resistance: A Time-Based Approach for Early Identification in First Episode Psychosis
Bibliographic record
Abstract
Although approximately 1/3 of individuals with schizophrenia are Treatment Resistant (TR), identifying these subjects prospectively remains challenging. The Treatment Response and Resistance in Psychosis working group defines <20% improvement as an indicator of TR, though its utility in First Episode Schizophrenia (FES) remains unknown. In a prospective cohort of FES (n = 129) followed up for 5 years, we evaluated two improvement thresholds for ‘probable TR’; <20% and <50% based on positive, negative, and total symptoms. We ascertained (1) the ecological validity (i.e., the ability to identify an expected subgroup of 1/3rd of patients); (2) the predictive validity (i.e., ability to predict poor global functioning) and (3) the clinical utility (association with clozapine use at the 5th year). Using the criteria of a total symptom reduction of <50% or negative symptom reduction of <20% resulted in ‘probable TR’ rates of 37% and 33%, respectively. Using <20% positive or total symptoms criteria resulted in very low rates, indicating minimal utility in FES. <50% total symptom criterion best predicted the global functioning over 5 years. Clozapine use was only predicted by positive symptom criterion. Prospective characterization of TRS is possible at 6 months after FES through a time-based approach using a 50% threshold for symptom change in treatment-adherent patients.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".