Clinical Inertia in the Diagnosis and Management of Hypertension Following Ambulatory Blood Pressure Monitoring
Bibliographic record
Abstract
BACKGROUND: Clinical inertia is common after office blood pressure (BP) is high. Little is known about clinical inertia after ambulatory BP monitoring (ABPM). METHODS: This was an electronic health record-based retrospective cohort study of patients with high office BP (≥140/90 mm Hg) referred for ABPM at a medical center in New York City between 2016 and 2020. Diagnostic inertia was defined as clinicians not newly diagnosing or treating hypertension in patients with high ABPM (i.e., mean awake BP ≥135/85 mm Hg). Therapeutic inertia was defined as clinicians not intensifying treatment for patients with established hypertension after high ABPM. Multilevel modeling was used to assess patient and clinician characteristics associated with inertia. RESULTS: Among 329 patients without prior hypertension, 144 (44%) had high awake BP. Of these, diagnostic inertia occurred in 45 of 144 (31%). Among 239 patients taking antihypertensive medication, 141 (59%) had high awake BP. Of these, therapeutic inertia occurred in 73 of 141 (52%). In multilevel models, male gender (odds ratio [OR] 2.81, 95% confidence interval [CI] 1.11-7.08), lower awake systolic BP (SBP) (OR 0.73 per 5 mm Hg increase, 95% CI 0.53-1.00), and specialist vs. primary care clinician type (OR 4.57, 95% CI 1.78-11.75) were associated with increased diagnostic inertia. Increasing age (OR 1.16 per 5-year increase, 95% CI 1.00-1.28) and lower awake SBP (OR 0.82 per 5 mm Hg increase, 95% CI 0.66-0.95) were associated with increased therapeutic inertia. CONCLUSIONS: Diagnostic and therapeutic inertia were common after ABPM, particularly when awake SBP was near the threshold.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".