Measuring competence in central venous catheterization: a systematic-review
Bibliographic record
Abstract
OBJECTIVES: Central venous catheterization is a complex procedural skill. This study evaluates existing published tools on this procedure and systematically summarizes key competencies for the assessment of this technical skill. METHODS: Using a previously published meta-analysis search strategy, we conducted a systematic review of published assessment tools using the electronic databases PubMed, MEDLINE, Education Resource Information Center (ERIC), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica, and Cochrane Central Register of Controlled Trials. Two independent investigators abstracted information on tool content and characteristics. RESULTS: Twenty-five studies were identified assessing a total of 147 items. Tools used for assessment at the bedside (clinical tools) had a higher % of items representing "preparation" and "infection control" than tools used for assessment using simulation (67 ± 26% vs. 32 ± 26%; p = 0.003 for "preparation" and 60 ± 41% vs. 11 ± 17%; p = 0.002 for "infection control", respectively). Simulation tools had a higher % of items on "procedural competence" than clinical tools (60 ± 36% vs. 17 ± 15%; p = 0.002). Items in the domains of "Team working" and "Communication and working with the patient" were frequently under-represented. CONCLUSION: This study presents a comprehensive review of existing checklist items for the assessment of central venous catheterization. Although many key competencies are currently assessed by existing published tools, some domains may be under-represented by select tools.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".