Effectiveness of an HbA1c tracking tool on primary care managementof diabetes mellitus: glycaemic control, clinical practice andusability
Bibliographic record
Abstract
OBJECTIVE: To determine if a laboratory data report (the HbA1c Tracking Tool) could be used as an effective intervention to improve diabetes management. DESIGN: A longitudinal quasi-experimental cohort design was used to test the effectiveness of an HbA1c summary report sent to primary care physicians for all patients having HbA1c levels greater than 7%. SETTING: Moncton, New Brunswick, Canada. SAMPLE SELECTION: Administrative data from all adult patients with diabetes who had had at least two HbA1c measurements within the year prior to the initiation of the HbA1c Tracking Tool, and who had had five years of HbA1c measurements (2002-2007) overall was included. INTERVENTIONS: In March 2006 all primary care physicians began receiving HbA1c summary reports (through the HbA1c Tracking Tool) as a means to improving the management of diabetes. MAIN OUTCOME MEASURES: (a) patient glycaemic control as indicated by HbA1c levels, (b) physician adherence to practice guidelines as indicated by measuring the mean number of HbA1c tests ordered per patient per year, and (c) physician usage rates of the HbA1c Tracking Tool in clinical practice. RESULTS: The sample (n=955) was divided into three subgroups based on flagged HbA1c level (7-<8%, 8-9%, >9%). The strongest effect of the intervention was found in the two groups with the poorest glycaemic control. The effect was stronger in the >9% group (from 10.1 to 9.3%), than in the 8-9% group (a drop of 8.5 to 8.3%). Longitudinal analyses over a five-year period indicated the same findings. Patients were also found to receive more tests across time (from 2.45 tests per year to 3.0 across five years). In terms of usage, 92.1% of the physicians surveyed used the tool in their practice. CONCLUSION: Routinely collected hospital laboratory data can be used both as the basis for an information-based intervention and as a tool to monitor quality of diabetes care.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".