Methods to Achieve High Interrater Reliability in Data Collection From Primary Care Medical Records
Bibliographic record
Abstract
PURPOSE: We assessed interrater reliability (IRR) of chart abstractors within a randomized trial of cardiovascular care in primary care. We report our findings, and outline issues and provide recommendations related to determining sample size, frequency of verification, and minimum thresholds for 2 measures of IRR: the κ statistic and percent agreement. METHODS: We designed a data quality monitoring procedure having 4 parts: use of standardized protocols and forms, extensive training, continuous monitoring of IRR, and a quality improvement feedback mechanism. Four abstractors checked a 5% sample of charts at 3 time points for a predefined set of indicators of the quality of care. We set our quality threshold for IRR at a κ of 0.75, a percent agreement of 95%, or both. RESULTS: Abstractors reabstracted a sample of charts in 16 of 27 primary care practices, checking a total of 132 charts with 38 indicators per chart. The overall κ across all items was 0.91 (95% confidence interval, 0.90-0.92) and the overall percent agreement was 94.3%, signifying excellent agreement between abstractors. We gave feedback to the abstractors to highlight items that had a κ of less than 0.70 or a percent agreement less than 95%. No practice had to have its charts abstracted again because of poor quality. CONCLUSIONS: A 5% sampling of charts for quality control using IRR analysis yielded κ and agreement levels that met or exceeded our quality thresholds. Using 3 time points during the chart audit phase allows for early quality control as well as ongoing quality monitoring. Our results can be used as a guide and benchmark for other medical chart review studies in primary 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 | 0.449 | 0.545 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".