Treatment Resistance: A Time-Based Approach For Early Identification in First Episode Samples
Bibliographic record
Abstract
Background: Although approximately 1/3 of individuals with schizophrenia are Treatment Resistant (TR), identifying these subjects prospectively for early intervention remains challenging. The Treatment Response and Resistance in Psychosis (TRIPP; Howes et al, 2017) working group recently published consensus guidelines defining lack of response as a <20% improvement in symptoms. However, it is unclear whether these criteria are sensitive in First Episode Schizophrenia (FES). Method: Patients experiencing a first episode of psychosis referred to the Prevention and Early Intervention Program for Psychosis (PEPP) in London, Canada were followed-up with longitudinal symptom assessments. We evaluated two improvement thresholds for ‘probable TR’ classifications; <20% (as per TRIPP) and <50% to identify subjects satisfying ‘probable TR’ based on positive, negative, and total symptom domains. Results: Using the criterion of <50% total, or <20% negative symptom improvement, resulted in ‘probable TR’ rates of 37% and 33% respectively, with notable overlap between the 2 criteria (77% satisfying both). Using a 20% cut-off for positive and total symptoms resulted in very low rates of ‘probable TR’. Logistic regression analyses demonstrated that poor premorbid functioning, longer duration of untreated illness, and limited treatment response at months one and two were significantly associated with probable TR (<50% total symptom improvement). Conclusions: Our results suggest that probable TR may be identified at 6 months after FES using a time-based approach only by including negative symptoms (either alone, with a 20% improvement threshold, or in addition to positive symptoms, with a total 50% threshold) in the definition.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".