Subjective cognitive complaints in first episode psychosis: A focused follow-up on sex effect and alcohol usage
Bibliographic record
Abstract
A network of early psychosis-specific intervention programs at the University of Montreal in Montreal, Quebec, Canada, conducted a longitudinal naturalistic five-year study at two Urban Early Intervention Services (EIS). In this study, 198 patients were recruited based on inclusion/exclusion criteria and agreed to participate. Our objectives were to assess the subjective cognition complaints of schizophrenic patients assessed by Subjective Scale to Investigate Cognition in Schizophrenia (SSTICS) in their first-episode psychosis (FEP) in relation to their general characteristics. We also wanted to assess whether there are sex-based differences in the subjective cognitive complaints, as well as differences in cognitive complaints among patients who use alcohol in comparison to those who are abstainers. Additionally, we wanted to monitor the changes in the subjective complaints progress for a period of five years follow-up. Our findings showed that although women expressed more cognitive complaints than men [mean (SD) SSTICS, 28.2 (13.7) for women and 24.7 (13.2) for men], this difference was not statistically significant (r = −0.190, 95 % CI, −0. 435 to 0. 097). We also found that abstainers complained more about their cognition than alcohol consumers [mean (SD) SSTICS, 27.9 (13.4) for abstainers and 23.7 (12.9) for consumers], a difference which was statistically significant (r = −0.166, 95 % CI, −0. 307 to −0.014). Our findings showed a drop in the average score of SSTICS through study follow-up time among FEP patients. In conclusion, we suggest that if we want to set up a good cognitive remediation program, it is useful to start with the patients' demands. This demand can follow the patients' complaints. Further investigations are needed in order to propose different approaches between alcohol users and abstinent patients concerning responding to their cognitive complaints.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".