Student Attainment of Proficiency in a Clinical Skill: The Assessment of Individual Learning Curves
Bibliographic record
Abstract
The aims of this study were to determine if the learning curve cumulative summation test (LC-CUSUM) can differentiate proficiency in placing intravenous catheters by novice learners, and identify the cause of failure when it occurred. In a prospective, observational study design 6 undergraduate students with no previous experience of placing intravenous catheters received standardized training by a board certified veterinary anesthesiologist in intravenous catheter placement technique. Immediately following training, each student attempted 60 intravenous catheterizations in a dog mannequin thoracic limb model. Results were scored as a success or failure based upon completion of four specific criteria, and where catheter placement failure occurred, the cause was recorded according to pre-defined criteria. Initial acceptable and unacceptable failure rates were set by the study team and the LC-CUSUM was used to generate a learning curve for each student. Using 10% and 25% acceptable and unacceptable failure rates, 3 out of 6 students attained proficiency, requiring between 26 to 48 attempts. Applying 25% and 50% acceptable and unacceptable failure rates, 5 of 6 students obtained proficiency, requiring between 18 and 55 attempts. Wide inter-individual variability was observed and the majority of failed catheterisation attempts were limited to two of the four pre-defined criteria. These data indicate that the LC-CUSUM can be used to generate individual learning curves, inter-individual variability in catheter placement ability is wide, and that specific steps in catheter placement are responsible for the majority of failures. These findings may have profound implications for how we teach and assess technical skills.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".