Prediction of Vasovagal Syncope based on Body Vigilance and Alexithymia Variables
Bibliographic record
Abstract
Introduction: Vasovagal syncope is the most common type of syncope and recurrent syncope attacks can have a profound effect on the patients' quality of life. The aim of this study was to predict vasovagal syncope based on the variables of body vigilance and alexithymia. Methods: The present study was a case-control study. The statistical population of the present study included the patients with vasovagal syncope referred to Tehran Heart Center in the year of 1400. Fifty patients with vasovagal syncope and 54 healthy individuals were selected by purposive sampling. Data collection tools included the Body Vigilance Scale and Toronto Alexithymia Scale. Data analysis was performed by logistic regression analysis and using SPSS software version 16 at the level of 0.05. Results: The results of the Hosmer-Lemeshow Test (P-value = 0.255, χ2 = 3.07) showed good model fit. According to the results of logistic regression test, the coefficient estimate for the alexithymia -0.053 and the body vigilance was -0.017. Conclusion: Based on the results, in the patients with vasovagal syncope, by designing measures based on reducing body vigilance and alexithymia, they can be helped to improve and reduce syncope episode.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".