Factors Affecting Accreditation in Extracranial Carotid Ultrasound Studies by the Intersocietal Accreditation Commission
Bibliographic record
Abstract
Introduction: Accreditation of vascular laboratories has been shown to lead to overall improvements in quality patient care. However, there are still factors that needlessly delay the granting of full accreditation. We performed a retrospective analysis looking at factors associated with delays in accreditation in extracranial carotid artery ultrasound examinations by the Intersocietal Accreditation Commission - Vascular Testing (IAC-VT) division. Materials and Methods: We accessed an active database from the IAC-VT division between 2014 and 2020 and extracted data linked to vascular laboratory accreditation in extracranial carotid ultrasound studies. We used the ‘Delay” versus the “Grant” status as outcome and looked at the association with 18 metrics that are part of the application evaluation. We further used a modified Delphi method to determine the relative role played by either the technologist/sonographer or the interpreting physician for each metric. Statistical significance was evaluated by Chi-square. Results: A “Delay” status was assigned in 1638 (58.6%) out of 2794. Ten factors were noted to be significant univariate predictors of a “Delay” status. The three major factors were solely associated with the interpreting physician while adherence to technical factors showed mostly shared responsibility. Conclusion: This retrospective study indicated that the accreditation process is strongly dependent on interpreting physician performance. Targeted interventions may help decrease time and effort associated with the costs of the accreditation process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".