Assessment of central venous catheterization in a simulated model using a motion-tracking device: an experimental validation study
Bibliographic record
Abstract
BACKGROUND: Central venous catheterization (CVC) is a basic requirement for many medical specialties. Simulated training in CVC may allow the acquisition of this competency but few reports have established a valid methodology for learning and acquiring procedural skills for CVC. This study aims to validate the use of a tracking motion device, the imperial college surgical assessment device (ICSAD), by comparing it with validated global rating scales (GRS) to measure CVC performance in a simulated torso. METHODS: Senior year medical students, first and last year residents (PGY1, LYR), and expert anesthesiologists performed a jugular CVC assessment in a simulated model (Laerdal IV Torso). A validated GRS for objective assessment of technical skills and motion analysis by ICSAD was used. Statistical analysis was performed through Mann-Whitney and Kruskal-Wallis tests for construct validity and Spearman correlation coefficients between the ICSAD and GRS scores for concurrent validity between both. RESULTS: 32 subjects were recruited (10 medical students, 8 PGY1, 8 LYR and 8 experts). Total path length measured with ICSAD and GRS scores were significantly different between all groups, except for LYR compared to experts (p = 0.664 for GRS and p = 0.72 for ICSAD). Regarding jugular CVC procedural time, LYR and experts were faster than PGY1 and MS (p < 0.05). Spearman correlation coefficient was -0.684 (p < 0.001) between ICSAD and GRS scores. CONCLUSIONS: ICSAD is a valid tool for assessment of jugular CVC since it differentiates between expert and novice subjects, and correlates with a validated GRS for jugular CVC in a simulated torso.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".