Medical Record Review Conduction Model for Improving Interrater Reliability of Abstracting Medical-Related Information
Bibliographic record
Abstract
Medical record review (MRR) is often used in clinical research and evaluation, yet there is limited literature regarding best practices in conducting a MRR, and there are few studies reporting interrater reliability (IRR) from MRR data. The aim of this research was twofold: (a) to develop a MRR abstraction tool and standardize the MRR process and (b) to examine the IRR from MRR data. This study introduces the MRR-Conduction Model, which was used to implement a MRR, and examines the IRR between two abstractors who collected preinjury medical and psychiatric, incident-related medical and postinjury head symptom information from the medical records of 47 neurologically injured workers. Results showed that the percentage agreement was > or =85% and the unweighted kappa statistic was > or =.60 for most variables, indicating substantial IRR. An effective and reliable MRR to abstract medical-related information requires planning and time. The MRR-Conduction Model is proposed to guide the process of creating a MRR.
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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 | 0.383 | 0.539 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".