Effect of cariprazine on quality of life and attention in patients with persistent negative symptoms of schizophrenia – a post-hoc analysis
Bibliographic record
Abstract
answered by a panel of experts made up of 42 spanish psychiatrists dedicated to the field of study.In thematic area 1 (clinical symptomatology), a consensus was reached in 14 of the 17 items (82.4%).In topic area 2 (treatment factors), consensus was reached on 14 of the 16 items (87.5%).In subject area 3 (healthcare system and sociosanitary services), consensus was reached on all 14 items in the area (100%).In thematic area 4 (physical health and monitoring), a consensus was reached in the 14 items of the area (100%), and in thematic area 5 (clinical care in pandemia) there was also full agreement in the 3 items of the area.A broad consensus has been reached in relation to adapting the treatment of patients with schizophrenia admitted to inpatient units of psychiatry, in carrying out complementary health tests that assess the physical health of schizophrenia patients (electrocardiogram, complete blood count, cholesterol, triglycerides, glycated hemoglobin, liver and kidney function, thyroid hormones, prolactin, etc), implementing simple and well-tolerated regimens, and adapting admission units to pandemic situations (1).Less consensus has been evidenced regarding considering discharge from the psychiatric inpatient units in patients who express distancing from thoughts of suicide, and when they present negative and cognitive symptoms, these being areas that require further investigation.No consensus has been reached either that family associations participate in the care process of the psychiatric inpatients unit and that psychosocial resources have access to the Electronic Medical Record.The COVID-19 pandemic has created challenges for mental health professionals who provide care to patients with schizophrenia in psychiatric inpatient units.The group of experts highlighted the suitability of using telemedicine resources to facilitate regular patient-family and team-reference care circuit contact.Concerns for psychiatric inpatient units include the management of patients and staff who may have been exposed to COVID-19, and the implementation and monitoring of protocols that deal with the clinical care of inpatients with schizophrenia (2).References
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".