Cognitive load imposed by knobology may adversely affect learners' perception of utility in using ultrasonography to learn physical examination skills, but not anatomy
Bibliographic record
Abstract
Ultrasonography is increasingly used for teaching anatomy and physical examination skills but its effect on cognitive load is unknown. This study aimed to determine ultrasound's perceived utility for learning, and to investigate the effect of cognitive load on its perceived utility. Consenting first-year medical students (n = 137) completed ultrasound training that includes a didactic component and four ultrasound-guided anatomy and physical examination teaching sessions. Learners then completed a survey on comfort with physical examination techniques (three items; alpha = 0.77), perceived utility of ultrasound in learning (two items; alpha = 0.89), and cognitive load on ultrasound use [measured with a validated nine-point scale (10 items; alpha = 0.88)]. Learners found ultrasound useful for learning for both anatomy and physical examination (mean 4.2 ± 0.9 and 4.4 ± 0.8, respectively; where 1 = very useless and 5 = very useful). Principal components analysis on the cognitive load survey revealed two factors, "image interpretation" and "basic knobology," which accounted for 60.3% of total variance. Weighted factor scores were not associated with perceived utility in learning anatomy (beta = 0.01, P = 0.62 for "image interpretation" and beta = -0.04, P = 0.33 for "basic knobology"). However, factor score on "knobology" was inversely associated with perceived utility for learning physical examination (beta = -0.06; P = 0.03). While a basic introduction to ultrasound may suffice for teaching anatomy, more training may be required for teaching physical examination. Prior to teaching physical examination skills with ultrasonography, we recommend ensuring that learners have sufficient knobology skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".