Using psychometric ability to improve education in ultrasound‐guided regional anaesthesia: a multicentre randomised controlled trial
Bibliographic record
Abstract
The learning curve for novices developing regional anaesthesia skills, such as real-time ultrasound-guided needle manipulation, may be affected by innate visuospatial ability, as this influences spatial cognition and motor co-ordination. We conducted a multinational randomised controlled trial to test if novices with low visuospatial ability would perform better at an ultrasound-guided needling task with deliberate practice training than with discovery learning. Visuospatial ability was evaluated using the mental rotations test-A. We recruited 140 medical students and randomly allocated them into low-ability control (discovery learning), low-ability intervention (received deliberate practice), high-ability control, and high-ability intervention groups. Primary outcome was the time taken to complete the needling task, and there was no significant difference between groups: median (IQR [range]) low-ability control 125 s (69-237 [43-600 s]); low-ability intervention 163 s (116-276 [44-600 s]); high-ability control 130 s (80-210 [41-384 s]); and high-ability intervention 177 s (113-285 [43-547 s]), p = 0.06. No difference was found using the global rating scale: mean (95%CI) low-ability control 53% (95%CI 46-60%); low-ability intervention 61% (95%CI 53-68%); high-ability control 63% (95%CI 56-70%); and high-ability intervention 66% (95%CI 60-72%), p = 0.05. For overall procedure pass/fail, the low-ability control group pass rate of 42% (14/33) was significantly less than the other three groups: low-ability intervention 69% (25/36); high-ability control 68% (25/37); and high-ability intervention 85% (29/34) p = 0.003. Further research is required to determine the role of visuospatial ability screening in training for ultrasound-guided needle 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".