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Record W2364178590 · doi:10.1016/j.eurpsy.2016.01.845

Treatment of schizophrenia using tablet and smartphone based applications (Polish Study)

2016· article· en· W2364178590 on OpenAlexaboutno aff
Marek Krzystanek, Dariusz Krzeszowski, Karolina Jagoda, B. Radzio, K. Krysta

Bibliographic record

VenueEuropean Psychiatry · 2016
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)DeclarationCompliance (psychology)CognitionPsychiatryMedicineOutpatient clinicDrug compliancePsychologyPhysical therapyIntensive care medicineComputer scienceInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.304
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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