Developing a method to estimate practice denominators for a national Canadian electronic medical record database
Bibliographic record
Abstract
BACKGROUND: Calculating disease prevalence requires both a numerator (number of persons with a disease) and a matching denominator (the 'population at risk' being studied). Determining primary care practice denominators is challenging. OBJECTIVE: To develop and test a method to calculate primary care practice denominators. METHODS: We compared a 'corrected yearly contact group', or practice population, with the number of patients enrolled with practices. The yearly contact group was the set of patients with a visit noted in the electronic medical records during the past year. The correction factor was the proportion of patients that reported contacting their physician in the past year. Eighty-one physicians from Toronto and Kingston, Ontario, provided data. The main outcome measure was the ratio of practice population to the number of enrolled patients. Other measures included the change in ratio over 2 years, differences between locations, and differences by provider, practice and patient characteristics. RESULTS: The ratio of practice population to enrolled patients was 1.03 in 2010 (95% confidence interval 1.00 to 1.05) and 1.03 in 2011 (95% confidence interval 1.00 to 1.05). There was no change in the ratio over time. Ratios by location, provider or practice characteristics differed by less than 10%. There was a slight under-estimation of practice population for younger male patients and over-estimation for female patients. CONCLUSION: This method provided a denominator that was reasonably similar to the enrolled population and was stable over time and by location, provider and practice characteristics. In regions without patient enrollment, this may provide an estimate of practice denominators.
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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 | 0.030 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".