Treatment of schizophrenia using tablet and smartphone based applications (Polish Study)
Bibliographic record
Abstract
Relapses, deterioration of cognitive functioning, negative symptoms, neuroleptic resistance are the examples of many consequences of noncompliance in schizophrenia. In order to improve the compliance, schizophrenic patients treated in an outpatient department in a traditional way have been given an additional possibility of contacting their doctors with the use of a special application on a portable electronic device. Other functions of this application are possibilities of PANSS, Calgary and CGI measurements and cognitive trainings for the patients. This type of a remote contact with patients can be an effective tool in the work in an outpatient setting. The compliance was assessed using a telepsychiatric system, sending reminders: 1 hour before the planned dose to remind them that drug intake is approaching, and at the moment of intake to check if they took the drug. In general the compliance in the group of schizophrenic patients in remission is very low, however the telemedicine system improves the compliance in this group of patients, in which the compliance is the worst. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".