Defining a standard set of outcomes for patients with psychosis
Bibliographic record
Abstract
Abstract Background A working group (WG) of researchers, clinicians, and service users was created to develop a Standard Set of Psychotic Disorder Outcomes. This study describes the ICHOM framework to create a minimum Standard Set of outcomes for Psychotic Disorders. Methods The WG defined the scope of the project to include adolescents and adults meeting DSM-5 criteria for schizophrenia spectrum disorders or bipolar I disorder. A systematic literature review was conducted to extract patient outcomes. The WG then selected outcomes for this minimum standard set using an online modified Delphi consensus technique, throughout a series of video-conference calls. Outcome measures were identified through additional literature searches using a Terwee filter. These were evaluated by inclusion criteria, namely psychometric properties, feasibility of implementation, licensing fees and available translations. Results In total, 7074 studies were screened for outcome extraction. Outcomes were extracted from the 239 of these which passed eligibility criteria. The WG voted to include 17 outcomes, grouped into 7 domains (symptoms, treatment, recovery, functioning, quality of life, physical health and safety). A further 24 outcomes were identified for bipolar disorder, grouped within the same domains. Conclusions The outcomes recommended by the working group enable meaningful and standardized assessment of psychotic disorders worldwide. Adoption of the ICHOM framework can facilitate informed decision-making by healthcare providers, long term monitoring and improved global treatment outcomes in the long term. Key messages Standard set provides a valid and easy tool for healthcare workers for measuring outcomes. Value based health care and outcomes measures could globally improve healthcare and at the same time reduce healthcare cost.
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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 | 0.259 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.031 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".