Objective Assessment of Manual Skills and Proficiency in Performing Epidural Anesthesia—Video-Assisted Validation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Demand is growing for objective assessment of manual skills and competencies of invasive procedures. The aim of this study was to validate an objective tool for assessing residents' skill in performing epidural anesthesia by use of a global assessment scale and a 3-scale, 27-stage checklist. We wish to demonstrate that this tool can differentiate operators with different levels of training. METHODS: Second-year anesthesia residents were recruited. Their previous experience was assessed by questionnaire. They were repeatedly videotaped performing epidural anesthesia over a 6-month period. Videotaping was done in a blinded manner that masked the identity and level of training of the residents. Three blinded, independent examiners evaluated each session by use of a specifically devised assessment tool that consisted of a global rating scale and a 3-scale, 27-stage checklist to judge the skill level and grade the videotaped sessions. RESULTS: Twenty-one sessions by 6 residents were videotaped over 6 months. Interrater reliability for the different checklist and global-rating form items shows moderate to high degree of agreement for most stages. Total scores demonstrate almost perfect agreement (kappa/ICC +/- SE = 0.90 +/- 0.03 and 0.83 +/- 0.13, respectively; P < .0001) between examiners. To test whether higher total scores are associated with greater experience, a series of repeated-measures ANCOVAs were performed. In both the global-rating form and the checklist, a significant relation between total scores and epidurals done was found to exist (checklist: P < .0001; global rating: P < .0001). CONCLUSIONS: The results of our study show that scores on a system that consists of a global-rating form and a task-specific checklist had a significant relation to the number of epidural insertions performed (i.e., experience). The interrater reliability of these assessment tools was very strong. Evaluation of technical skills by an objective tool under direct observation, as opposed to laboratory setting, may create a more reliable standard of assessment. Furthermore, residency programs could use these evaluations to identify deficiencies in teaching programs and trainees who require extra instruction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".