An intervention to improve the interrater reliability of clinical EEG interpretations
Bibliographic record
Abstract
Several studies have noted modest interrater reliability of clinical electroencephalogram (EEG) interpretations. Moreover, no study to date has investigated a means to improve the observed interrater agreement. The purpose of the present study was to examine (i). the interrater reliability of EEG interpretations among three raters (two psychiatrists and one pediatrician); and (ii). how to improve the reliability by establishing a consensus guideline for EEG interpretation. Three raters, two psychiatrists and a pediatrician, interpreted 100 consecutive EEG recorded at Tajimi General Hospital. After discussing the results of the first trial, the raters established a consensus guideline for EEG interpretation. They then interpreted 50 consecutive EEG recorded at Nagoya City University Hospital following this guideline. Kappa for global judgment of EEG abnormality in three grades (abnormal/borderline/normal) was 0.42 on the first and 0.63 on the second trial. Kappa significantly improved by using the guideline (P = 0.004). It is suggested that discussing and establishing the consensus guideline among the raters offers a feasible method to improve interrater reliability in clinical EEG interpretations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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; 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".