Foundation of algorithm of medical and psychological support of adolescents with primary arterial hypertension
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".