Experience Is Not Enough
Bibliographic record
Abstract
BACKGROUND: Invasive procedures such as epidural anesthesia carry risks for complications such as erroneous placement arising from inadequate manual skills and infection secondary to breaches in aseptic technique. Although it is assumed that improvement in aseptic technique parallels improved dexterity, this assertion remains unproven. The aim of this study was to determine whether increased proficiency in the manual skills for epidural anesthesia is associated with improved aseptic technique. METHODS: Second-year anesthesia residents were repeatedly videotaped performing epidural anesthesia over 6-month periods. Three independent examiners blinded to the level of training of the residents evaluated the procedures for manual skills and aseptic technique. Each procedure was graded using a manual skills checklist, a global rating scale, and an aseptic technique checklist. The main outcome measures were the scores for these three tools. RESULTS: Thirty-five sessions were videotaped over 1 yr. Interrater reliability was nearly perfect. A strong positive association was found between increased experience and manual skills, as reflected by the scores achieved on both the manual skills checklist and the global rating scale. In contrast, a nonsignificant or very weak correlation was found between the aseptic technique checklist total scores and the number of epidurals performed. CONCLUSION: Manual skills for invasive procedures improved with increasing experience, but aseptic technique did not, despite formal teaching. These findings reflect major gaps in the understanding and teaching of the principles of aseptic technique, most likely due to lack of structured training. Educational initiatives are needed to correct these teaching gaps.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".