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Foundation of algorithm of medical and psychological support of adolescents with primary arterial hypertension

2018· article· en· W2906874763 on OpenAlexaboutno aff
О.М. Місюра, М. В. Хайтович, Н.М. Кухта, L.I. Misiura, С. Д. Максименко

Bibliographic record

VenueFamily Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaAnxietyDepression (economics)Blood pressureToronto Alexithymia ScaleClinical psychologyPsychologyMedicineEssential hypertensionPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The objective: of this work was to identify the frequency of increased anxiety, depression and alexithymia in adolescents with primary hypertension and to develop an algorithm for their medical and psychological support.Materials and methods. 91 children (21 girls and 70 boys) aged 10 to 17 y.o. (on average 14,65±1,52 y.o.) were examined. Diagnosis was verified by 24-hour blood pressure monitoring using monitors ABM-04 (Meditech,Hungary). Two groups were formed: I – 60 adolescents (11 girls and 49 boys) with stable and labile arterial hypertension and II – 31 adolescents (10 girls and 21 boys) – control group. Psychological study included identification of state and trait anxiety by Spielberger-Khanin test, alexithymia – using Toronto Alexithymia Scale, depression – by Zung Self-Rating depression scale.Results. Moderate statr anxiety was noted in 50%, and high – in 28.6% adolescents with primary hypertension; moderate and high trait anxiety respectively in 59,8% and 35,7%; alexithymia and risk of alexithymia – respectively in 21,4% and 31%, depression only 2,4%. On average, these emotional characteristisc did not differ from those of the control group. Taking into account the given and results of previous researches, the algorithm of medical and psychological support of adolescents with primary hypertension was developed and introduced.Conclusion. In the majority of adolescents with primary hypertension, there is anxiety and/or high anxiety that requires psychological correction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.070
GPT teacher head0.363
Teacher spread0.293 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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