Surgeon interrater reliability in the endoscopic assessment of cistern scarring and aqueduct patency
Bibliographic record
Abstract
OBJECTIVE The success of endoscopic third ventriculostomy with choroid plexus cauterization may have associations with age, etiology of hydrocephalus, previous shunting, cisternal scarring, and possibly aqueduct patency. This study aimed to measure interrater reliability among surgeons in identifying cisternal scarring and aqueduct patency. METHODS Using published definitions of cistern scarring and aqueduct patency, 7 neuroendoscopists with training from Dr. Warf in Uganda and 7 neuroendoscopists who were not trained by Dr. Warf rated cistern status from 30 operative videos and aqueduct patency from 26 operative videos. Interrater agreement was calculated using Fleiss' kappa coefficient (κ). Fisher's 2-tailed exact test was used to identify differences in the rates of agreement between the Warf-trained and nontrained groups compared with Dr. Warf's reference answer. RESULTS Aqueduct status, among all raters, showed substantial agreement with κ = 0.663 (confidence interval [CI] 0.626-0.701); within the trained group and nontrained groups, there was substantial agreement with κ = 0.677 (CI 0.593-0.761) and κ = 0.631 (CI 0.547-0.715), respectively. The identification of cistern scarring was less reliable, with moderate agreement among all raters with κ = 0.536 (CI 0.501-0.571); within the trained group and nontrained groups, there was moderate agreement with κ = 0.555 (CI 0.477-0.633) and κ = 0.542 (CI 0.464-0.620), respectively. There was no statistically significant difference in the amount of agreement between groups compared with Dr. Warf's reference. CONCLUSIONS Regardless of training with Dr. Warf, all neuroendoscopists could identify scarred cisterns and aqueduct patency with similar reliability, emphasizing the strength of the published definitions. This makes the identification of this risk factor for failure generalizable for surgical decision making and research studies.
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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 | 0.078 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".