Optimizing Residents’ Performance of Lumbar Puncture: An RCT Comparing the Effect of Preparatory Interventions on Performance and Self-Confidence
Bibliographic record
Abstract
BACKGROUND: Lumbar puncture is often associated with uncertainty and limited experience on the part of residents; therefore, preparatory interventions can be essential. There is growing interest in the potential benefit of videos over written text. However, little attention has been given to whether the design of the videos impacts on subsequent performance. OBJECTIVE: To investigate the effect of different preparatory interventions on learner performance and self-confidence regarding lumbar puncture (LP). DESIGN: Randomized controlled trial in which participants were randomly assigned to one of three interventions as preparation for performing lumbar puncture: 1) goal- and learner-centered video (GLV) presenting procedure-specific process goals and learner-centered information; 2) traditional video (TV) providing expert-driven content, but no process goals; and 3) written text (WT) with illustrations. PARTICIPANTS AND MAIN MEASURES: Participants were PGY-1 doctors without LP experience. After the preparatory intervention, participants performed an LP in a simulated setting with a standardized patient and an assistant. Two content experts, blinded to participant group allocation, assessed video recordings of the performance using the Lumbar Puncture Assessment Tool (LumPAT) and an overall global rating. Participants rated their self-confidence immediately prior to performing the procedure. The primary outcome was the difference in LumPAT scores among groups. KEY RESULTS: A total of 110 PGY-1 doctors were included. Results demonstrated significant differences in LumPAT mean scores among the three groups: GLV, 42.8; TV, 40.6; WT, 38.1 (p = 0.01). The global rating scores were highest in the GLV group (p = 0.026). Self-confidence scores differed significantly among the three groups (p = 0.003), with the TV group scoring the highest. There were no significant correlations between self-confidence scores and performance scores in any of the groups. CONCLUSION: A video designed with procedure-specific process goals and learner-centered information resulted in better subsequent LP performance than a traditionally designed video or written text. Participants' self-confidence was not predictive of their actual performance.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".