Data discipline in electronic medical records
Bibliographic record
Abstract
Objective To evaluate the transformation in smoking status documentation after implementing a standardized intake tool as part of a primary care smoking cessation program. Design A before-and-after evaluation of smoking status documentation was conducted following implementation of a smoking assessment tool. To evaluate the effect of the intervention, the Canadian Primary Care Sentinel Surveillance Network was used to extract aggregate smoking data on the study cohort. Setting Academic primary care clinic in Kingston, Ont. Participants A total of 7312 primary care patients. Interventions As the first phase in a primary care smoking cessation program, a standardized intake tool was developed as part of a vital signs screening process. Main outcome measures Documented smoking status of patients before implementation of the intake tool and documented smoking status of patients in the 6 months after its implementation. Results Following the implementation of the standardized intake tool, there was a 55% ( P < .001; 95% CI 0.53 to 0.56) increase in the proportion of patients with a completed smoking status; more than 1100 former smokers were identified and the documented smoking rate in this cohort increased from 4.4% to 16.2%. Conclusion This study shows that the implementation of an intake tool, integrated into existing clinical operational structures, is an effective way to standardize clinical documentation and promotes the optimization of electronic medical records.
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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 | 0.057 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".