Towards a better understanding of primary negative symptoms: A longitudinal study in first-episode psychosis
Bibliographic record
Abstract
Background: Schizophrenia is characterized by positive (hallucinations, delusions) and negative (blunted affect, avolition) symptoms. Negative symptoms can be either primary (central to the illness) or secondary (induced by positive symptoms, depression, or extrapyramidal symptoms, for example). Primary negative symptoms still represent an unmet therapeutic need. This project set out to increase our understanding of these core symptoms by: 1) exploring the proportion of primary and secondary negative symptoms among those who did not remit; 2) examining medication adherence and clinical insight (awareness of mental illness, belief in response to medication, and belief in need for treatment) in relation to primary negative symptoms; and 3) confirming previous neuroimaging markers of remission and exploring for markers of primary negative symptoms. Participants: The final sample included 385 first-episode of psychosis (275 with schizophrenia or related spectrum-disorder) clients treated from January 2003 through April 2015 at the Prevention and Early Intervention Program for Psychoses at the Douglas Mental Health University Institute in Montreal, Canada. For the neuroimaging data, there were 101 first-episode of schizophrenia clients who completed a baseline MRI scan, of which, 75 completed a 1-year follow-up scan. Main outcome measures: Remission was defined as achieving a global rating of mild or less on eight core symptoms (four positive and four negative) and maintained for six months (Andreasen et al. (2005) Am J Psychiatry, 162, 441-449). Primary negative symptoms (PNS) was defined as a global rating of moderate or worse severity on one negative symptom sustained for six months in the absence of clinically relevant positive, depressive, and extrapyramidal symptoms (Hovington et al. (2012) BMC Psychiatry, 12, 1-11). Results: After one year of treatment, 20% of clients were considered to be full remission. Among the Non-Remitted clients, 40% presented with PNS and 28% with secondary negative symptoms (2nd-NS). Similar proportions were found after two years of treatment and 1-year outcome significantly predicted 2-year outcome. Clients with PNS and 2nd-NS displayed poorer insight over the first year of treatment compared to other clients. Insight did not alter as a function of medication adherence among the PNS clients, but did among the other clients with a significant effect observed for 'belief in need for treatment'. Finally, smaller hippocampal tail and parahippocampal cortex (PHC) volumes were verified as markers of not achieving remission. Compared to the other Non-Remitted clients, those with PNS had a significantly smaller PHC volume but did not differ in hippocampal tail volume. Moreover, there was a significant decrease in right PHC volume in the PNS clients over the one year follow-up period with a trend-level decrease in the left PHC. Conclusions: A large proportion of unremitted clients presented with PNS. In contrast to the current literature, clients with PNS do appear amenable to treatment; however, treatments for PNS are inadequate and newer, more efficacious treatments are needed. A smaller PHC volume may represent a distinct neurobiological marker for PNS that could guide future research in developing target-specific treatments. The concept of remission may need to be reformulated to account for those presenting with PNS.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".