Differences in Emotion Processing in Patients With Essential and Secondary Hypertension
Bibliographic record
Abstract
BACKGROUND: An impaired ability to experience and express emotions, known as alexithymia, has previously been associated with hypertension. Alexithymia and related emotion-processing variables, however, have never been examined as a function of the type of hypertension, essential (EH) or secondary (SH). METHODS: Our working hypothesis was that if dysregulated emotional processes play a key neurobiological role in EH, they would be less present in hypertension due to specific medical causes or SH. A total of 98 consecutive hypertensive patients (73 EH, 25 SH) with similar blood pressure levels completed two complementary measures of emotion processing: the 20-item Toronto Alexithymia Scale (TAS-20) and the Levels of Emotional Awareness Scale (LEAS). RESULTS: After controlling for confounding variables, LEAS score was lower in EH than SH (estimated means: 46.4 vs. 52.0; P = 0.028; effect size 0.52). TAS-20 scores did not differentiate EH from SH, but the differences were in the expected direction, with an effect size of 0.34 for TAS-20 total score. Neither psychometric measure was associated with the duration of hypertension or the presence of cardiovascular (CV) complications. CONCLUSIONS: These results are consistent with a contribution of an emotional or psychosomatic component in EH and may have practical implications for the nonpharmacological management of hypertension. They also demonstrate the utility of complementary measures of emotion processing in medically ill patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".