Intrusion errors in explicit memory: Their differential relationship with clinical and social outcome in chronic schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Memory deficits might account for clinical and adaptive differences between groups of patients with chronic schizophrenia. We investigated the qualitative factors of memory that influence clinical and social status. METHODS: Psychosocial functioning, clinical symptoms, and memory function were assessed in 99 patients at four time points over a 16-month period using recall scores for semantically related words, unrelated words, paired associated learning, and word span. An initial cluster analysis using symptom assessment data from all four time points divided the sample into three groups: patients with low symptoms ratings that remained stable throughout the study period (low symptom-stable group - LSSG; N=51); patients with initially high symptoms ratings that subsequently improved (high symptom-improved group - HSIG; N=32); and patients with initially high symptoms ratings that deteriorated during the follow-up (high symptom-deteriorated group - HSDG; N=16). RESULTS: Memory was better preserved in LSSG compared to HSIG and HSDG patients. Recall performance was generally better for semantically related words than for unrelated words but the difference between LSSG and the two other groups was more constant over time for semantically related words. Extra-list errors variable was positively correlated with three PANSS measures (r=.25-.47). Also, the extra-list errors scores were correlated with the Magical Ideation Scale (r=.34-.39). Memory scores (global explicit, unrelated, related) were significantly and positively correlated with independent living skills (r=.26-.55) and the extra-list errors were negatively correlated with both social support and independent living skills (r=-.29 and r=-.46, respectively). All groups showed a reduction in extraneous false recognition errors/intrusions (FRIs) over time with the HSIG showing the greater change. HSIG and HSDG patients committed slightly more FRIs in recall tasks (extraneous information) than LSSG patients. CONCLUSION: Memory performance is better in patients presenting with less severe symptomatology. The extent to which FRIs reduce over time in patients with schizophrenia is a novel finding.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".