Using electronic patient records to inform strategic decision making in primary care
Bibliographic record
Abstract
Although absolute risk of death associated with raised blood pressure increases with age, the benefits of treatment are greater in elderly patients. Despite this, the 'rule of halves' particularly applies to this group. We conducted a randomised controlled trial to evaluate different levels of feedback designed to improve identification, treatment and control of elderly hypertensives. Fifty-two general practices were randomly allocated to either: Control (n=19), Audit only feedback (n=16) or Audit plus Strategic feedback, prioritising patients by absolute risk (n=17). Feedback was based on electronic data, annually extracted from practice computer systems. Data were collected for 265,572 patients, 30,345 aged 65-79. The proportion of known hypertensives in each group with BP recorded increased over the study period and the numbers of untreated and uncontrolled patients reduced. There was a significant difference in mean systolic pressure between the Audit plus Strategic and Audit only groups and significantly greater control in the Audit plus Strategic group. Providing patient-specific practice feedback can impact on identification and management of hypertension in the elderly and produce a significant increase in 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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".