The impact of the COVID-19 Pandemic on hypertension phenotypes (ESH ABPM COVID-19 study)
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic had a major impact on medical care. This study evaluated the influence of the pandemic on blood pressure (BP) control and hypertension phenotypes as assessed by office and 24-hour ambulatory BP monitoring (ABPM). DESIGN AND METHODS: Data were collected from 33 centers including Excellence Centers of the European Society of Hypertension. Two groups of patients with treated hypertension were compared. Pandemic group: including participants who had ABPM twice - at visit 2 during the COVID-19 pandemic and visit 1 performed 9-15 months prior to visit 2. Pre-pandemic group: had ABPM at two visits, performed before the pandemic within 9-15 months interval. We determined the following hypertension phenotypes: masked hypertension, white coat hypertension, sustained controlled hypertension (SCH) and sustained uncontrolled hypertension (SUCH). We analyzed the prevalence of phenotypes and their changes between visits. RESULTS: Data of 1419 patients, 616 (43 %) in the pandemic group and 803 (57 %) in the pre-pandemic group, were analyzed. At baseline (visit 1), the prevalence of hypertension phenotypes did not differ between groups. In the pandemic group, the change in hypertension phenotypes between two visits was not significant (p = 0.08). In contrast, in the pre-pandemic group, the prevalence of SCH increased during follow-up (28.8 % vs 38.4 %, p < 0.01) while the prevalence of SUCH decreased (34.2 % vs 27.8 %, p < 0.01). In multivariable adjusted analysis, the only factor influencing negative changes of hypertension phenotypes was the COVID-19 pandemic period. CONCLUSION: These results indicate a negative impact of the COVID-19 pandemic on BP control assessed by hypertension phenotypes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".