Clinical Guidance on the Identification and Management of Treatment-Resistant Schizophrenia
Bibliographic record
Abstract
Treatment-resistant schizophrenia (TRS) occurs in approximately 30% of individuals diagnosed with schizophrenia. The identification and management of TRS in clinical practice are inconsistent and not evidence based. No established clinically relevant criteria for defining and treating TRS exist, although guidelines have been promulgated for clozapine use among TRS patients. This report summarizes the consensus from a roundtable that focused on defining and identifying TRS, pathways to treatment resistance, current treatments, unmet needs, and disease burden. Nine clinical experts in schizophrenia and TRS participated in a closed meeting on June 23, 2017, sponsored by Lundbeck, at which published literature in key areas of TRS research was reviewed. The findings from published studies were synthesized by experts in each area and presented to the group for review and discussion. It was agreed that inadequate response to 2 different antipsychotics, each taken with adequate dose and duration, is required to establish TRS. This recommendation is consistent with guidelines for clozapine use. For each trial, objective symptom measures should be used to assess treatment response, with medication adherence ensured. Once nonresponse is established (after ≥ 12 weeks for positive symptoms [2 trials of ≥ 6 weeks]), the treatment plan should be reevaluated and alternative pharmacologic or nonpharmacologic treatments considered. With increased awareness, those involved in the care of patients with schizophrenia will be able to identify TRS earlier in its course, thus supporting more informed treatment decisions by clinicians, patients, and caregivers to reduce the overall disease burden.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".