Dimensional characteristics of persistent negative symptoms in schizophrenia and their relationships with schizotypy in first-degree relatives
Bibliographic record
Abstract
PURPOSE OF THE ARTICLE: Schizophrenia with persistent negative symptoms (PNS) may have different characteristics regarding negative symptom dimensions and heritability patterns. This study aimed to investigate the dimensional characteristics of PNS and their relationships with schizotypal features in first-degree relatives (FDRs). MATERIALS AND METHODS: The study included 142 patients, 142 FDRs, and 71 healthy controls (HC). Patients were evaluated with the Positive and Negative Symptom Scale (PANSS), Brief Negative Symptom Scale (BNSS), Calgary Depression Scale for Schizophrenia (CDSS), and Simpson-Angus Scale (SAS). Schizotypy Personality Questionnaire was applied to FDR and HC groups. Clinical symptoms were compared between primary-PNS, secondary-PNS, and non-PNS groups. In addition, schizotypy scores were compared between FDRs and HCs. Then, the relationship between the symptoms of the patients in the PNS group and the schizotypy scores of their relatives was evaluated by multiple regression analysis. RESULTS: All negative symptom dimension scores were similar in primary-PNS and secondary-PNS and lowest in non-PNS. PNS-FDR had higher in all schizotypy scores than non-PNS-FDR and HC, except for lack of close friends and social anxiety. In the PNS group, positive symptom severity and PANSS experiential deficit scores significantly predicted positive and negative schizotypy scores in relatives. Negative schizotypy was associated with asociality. CONCLUSIONS: The PNS is likely a subtype in which the genetic basis of negative symptoms is stronger and is associated with genetic abnormalities shared by positive and negative schizotypy dimensions in relatives. Family-based genetic studies will be beneficial in enlightening the genetic etiology of PNS.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".