F128. CLINICAL SETTINGS AS A SOURCE OF HETEROGENEITY IN SCHIZOPHRENIA
Bibliographic record
Abstract
Variability and replication failures in research and treatment findings are common in literatures on complex and heterogeneous psychiatric syndromes. Clinical settings and programs where patients are recruited for studies represent a possible, but seldom considered or controlled, source of this variability. For example, Ismail and colleagues (2017) found significant differences in the prevalence of depression in patients with mild cognitive impairment (MCI) across settings. In terms of schizophrenia, patients are serviced and recruited in a wide spectrum of settings that range from active rehabilitation and case management programs to minimal support and medication management to inpatient settings. Our basic question was: do patients vary in basic demographic, clinical, cognitive and functional outcome findings across settings, suggesting recruitment sites as a potential source of data variability in research studies? We studied 156 patients meeting DSM-IV criteria for schizophrenia or schizoaffective disorder at settings in the Hamilton and Greater Toronto urban regions in Ontario, Canada. Settings included 1 active outpatient vocational rehabilitation/case management program (n=60), 4 outpatient medication/case management/support programs (n=78), 1 non-medical psychosocial support program (n =10) and 2 inpatient short term stay settings (n=8). In addition, 74 community volunteers screened for psychiatric history were included as a control group. Measures obtained on all participants included: basic demographic data, performance on CVLT-II (Delis et al., 2000), WRAT Reading score, WAIS-III subtests, total score on the University of California Performance Skills Assessment (UPSA) and the global composite score of the Multidimensional Scale of Independent Functioning (MSIF, Jaeger et al., 2003). Patient participants’ clinical status was indexed with medication and hospitalization history as well as with the Positive and Negative Syndrome Scale (PANSS, Kay et al., 2015). One-way ANOVA with corrected pair-wise post hoc testing revealed significant differences between settings in terms of diagnosis (schizophrenia versus schizoaffective disorder) (P=0.024), symptom severity for paranoia (PANSS) (P=0.031) and medication status (e.g. anxiolytics) (P=0.029), and functional skills (UPSA) and community independence (MSIF). Examples include UPSA comprehension/planning (P=0.003) and MSIF support global ratings (P=0.014). The ANOVA did not include the control group. The main findings of this study confirm significant heterogeneity in key characteristics of schizophrenia patients drawn from different clinical settings. This heterogeneity may be a source of inconsistency in study outcomes, making replication more difficult. For example, patients in active rehabilitation settings may demonstrate greater functional competence than patients drawn from other settings. At the same time, it is noteworthy that cognitive impairment may be less variable across studies, thereby leading to inconsistency in reports relating cognition to functionality. Accordingly, the nature of the setting where patients are recruited for studies should be considered and controlled in research on functional outcome in schizophrenia.
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 | 0.065 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".