Face and content validity of three assessment tools developed to evaluate cerebral angiography performance (536.4)
Bibliographic record
Abstract
Cerebral angiography (CA) is considered the gold standard for diagnosing primary neurovascular diseases. Although guidelines have been established to ensure adequate CA experience before independent practice, current tools used to assess CA performance during training have questionable validity and reliability. The initial phases of validation often involve determining whether the tool appears to measure the construct of interest (face validity; FV) and whether the items in the tool cover a representative sample of the construct (content validity; CV). This study proposes to establish the FV and CV of 3 assessment tools (task‐specific checklist, error‐based scale, and global rating scale) developed to evaluate CA performance. FV and CV were established by asking expert catheter‐based physicians to judge whether each tool appears to be a practical and pertinent measure of CA performance and to evaluate the appropriateness of each tool item. Based on experts’ recommendations, each tool was modified to improve its ease‐of‐use, organization, and clarity. Confirmation of the FV and CV of the 3 tools will contribute to ensuring that future assessment of CA performance is both accurate and consistent. Grant Funding Source : Supported by MITACS
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".