Quality indicators to measure blood pressure management over a timeinterval
Bibliographic record
Abstract
BACKGROUND: Quality indicators are an important part of the primary care landscape, but focus strongly on point-in-time measurements, such as a patient's last blood pressure (BP) measurement. There is a larger space of possible measurements, including ones that more explicitly consider management over an interval of time. OBJECTIVE: To determine the predictive abilities of five different quality indicators related to poor BP control. METHODS: Data from two New Zealand general practices was analysed on five BP control indicators for patients with diagnosed hypertension: 1) last BP high (>150/90 mmHg); 2) last BP high or no BP measurement; 3) two or more consistently high BP measurements for ≥ 90 days; 4) a high BP then lapse of >120 days in BP measurement; and 5) antihypertensive medication possession ratio (MPR) of <80%. Probability that a patient would be identified by each indicator for the nine-month evaluation period ending 31 March 2009 was computed for each indicator one quarter, two quarters and three quarters prior to this date. Associations among the five indicators for the evaluation period were also calculated. RESULTS: Positive predictive value (PPV) of indicators for the same indicator nine months later ranged from 27% (last BP high) to 64% (MPR). PPVs among the five measures with respect to the same time period ranged from 9% to 77% (median 33%). CONCLUSIONS: Modest PPVs between indicators suggest the importance of considering multiple indicators to incentivise best management across diverse aspects of BP control.
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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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".