Evaluating inter-and intra-rater reliability in the bronchoscopic grading of burn inhalation injury: The iBRONCH-BII study
Bibliographic record
Abstract
BACKGROUND: The evidence that the severity of burn inhalation injury (BII) impacts clinical outcomes is inconsistent. This may be due to misclassification arising from the subjectivity in bronchoscopically grading BII using systems such as the Abbreviated Injury Score (AIS). This study aimed to evaluate inter- and intra-rater reliability in the grading of BII using the AIS. METHODOLOGY: In a cohort study, specialist burns clinicians (n = 17) and novices (n = 10) graded sixteen BII bronchoscopic images using the AIS during an online meeting. Inter-rater reliability was evaluated using the Kappa statistic (k), with values < 0.60 considered clinically inadequate. The grade rating process was repeated after seven days to evaluate intra-rater reliability. Evaluation of reliability in the grading of BII bronchoscopy reports was conducted as a sensitivity analysis. RESULTS: Amongst all raters, inter-rater reliability was low for grading images (k = 0.30, 95 % confidence interval (CI): 0.29-0.31). Intra-rater reliability was higher than inter-rater reliability, but was still low, with median image grade rate k = 0.45 (interquartile range [IQR]:0.24-0.53). Intensivists demonstrated the highest rater reliability. CONCLUSION: Reliability in rating the grade of BII by bronchoscopic images was clinically inadequate. Strategies to improve the reliability of reporting the grade of BII are required.
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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 | 0.188 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".