Validity Evidence for the Neuro-Endoscopic Ventriculostomy Assessment Tool (NEVAT)
Bibliographic record
Abstract
BACKGROUND: Growing demand for transparent and standardized methods for evaluating surgical competence prompted the construction of the Neuro-Endoscopic Ventriculostomy Assessment Tool (NEVAT). OBJECTIVE: To provide validity evidence of the NEVAT by reporting on the tool's internal structure and its relationship with surgical expertise during simulation-based training. METHODS: The NEVAT was used to assess performance of trainees and faculty at an international neuroendoscopy workshop. All participants performed an endoscopic third ventriculostomy (ETV) on a synthetic simulator. Participants were simultaneously scored by 2 raters using the NEVAT procedural checklist and global rating scale (GRS). Evidence of internal structure was collected by calculating interrater reliability and internal consistency of raters' scores. Evidence of relationships with other variables was collected by comparing the ETV performance of experts, experienced trainees, and novices using Jonckheere's test (evidence of construct validity). RESULTS: Thirteen experts, 11 experienced trainees, and 10 novices participated. The interrater reliability by the intraclass correlation coefficient for the checklist and GRS was 0.82 and 0.94, respectively. Internal consistency (Cronbach's α) for the checklist and the GRS was 0.74 and 0.97, respectively. Median scores with interquartile range on the checklist and GRS for novices, experienced trainees, and experts were 0.69 (0.58-0.86), 0.85 (0.63-0.89), and 0.85 (0.81-0.91) and 3.1 (2.5-3.8), 3.7 (2.2-4.3) and 4.6 (4.4-4.9), respectively. Jonckheere's test showed that the median checklist and GRS score increased with performer expertise ( P = .04 and .002, respectively). CONCLUSION: This study provides validity evidence for the NEVAT to support its use as a standardized method of evaluating neuroendoscopic competence during simulation-based training.
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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 | 0.109 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".