Examining intra-rater and inter-rater response agreement: A medical chart abstraction study of a community-based asthma care program
Bibliographic record
Abstract
BACKGROUND: To assess the intra- and inter-rater agreement of chart abstractors from multiple sites involved in the evaluation of an Asthma Care Program (ACP). METHODS: For intra-rater agreement, 110 charts randomly selected from 1,433 patients enrolled in the ACP across eight Ontario communities were re-abstracted by 10 abstractors. For inter-rater agreement, data abstractors reviewed a set of eight fictitious charts. Data abstraction involved information pertaining to six categories: physical assessment, asthma control, spirometry, asthma education, referral visits, and medication side effects. Percentage agreement and the kappa statistic (kappa) were used to measure agreement. Sensitivity and specificity estimates were calculated comparing results from all raters against the gold standard. RESULTS: Intra-rater re-abstraction yielded an overall kappa of 0.81. Kappa values for the chart abstraction categories were: physical assessment (kappa 0.84), asthma control (kappa 0.83), spirometry (kappa 0.84), asthma education (kappa 0.72), referral visits (kappa 0.59) and medication side effects (kappa 0.51). Inter-rater abstraction of the fictitious charts produced an overall kappa of 0.75, sensitivity of 0.91 and specificity of 0.89. Abstractors demonstrated agreement for physical assessment (kappa 0.88, sensitivity and specificity 0.95), asthma control (kappa 0.68, sensitivity 0.89, specificity 0.85), referral visits (kappa 0.77, sensitivity 0.88, specificity 0.95), and asthma education (kappa 0.49, sensitivity 0.87, specificity 0.77). CONCLUSION: Though collected by multiple abstractors, the results show high sensitivity and specificity and substantial to excellent inter- and intra-rater agreement, assuring confidence in the use of chart abstraction for evaluating the ACP.
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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 | 0.136 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".