Case logging habits of integrated vascular surgery residents
Bibliographic record
Abstract
ObjectiveCurrent research suggests a significant discrepancy in integrated vascular resident (IVR) vs vascular fellow case numbers by the completion of training. Our objective was to understand how IVRs log cases over the course of their training. Additionally, we sought to assess whether IVRs receive any formal training on appropriate case logging techniques or their requirements for graduation.MethodsThis study was approved by the Association of Program Directors in Vascular Surgery; a survey was sent out to all current IVRs nationwide as well as to trainees who graduated in July of 2020. Demographic variables and training program data were collected in addition to assessing trainee knowledge of graduation requirements. Trainees where then given common case scenarios and asked how they would log the case.ResultsOf the 418 residents identified from the Association of Program Directors in Vascular Surgery program roster for the 2019-2020 and 2020-2021 academic years, 32% of individuals participated in the survey. Only one-quarter of the respondents logged all cases performed, with the major cited reason for not logging cases being the low level of involvement. Of the residents, 62% reported receiving formal instruction on how to log cases appropriately, with the majority of trainees logging cases either weekly (37%) or monthly (31%). Sixty-three percent of respondents knew that 250 major vascular cases were required to graduate; however, 71% of residents thought 250 major vascular cases were required to be completed during their chief year. Seventy-five percent of trainees incorrectly believed that only primary procedures logged count toward graduation requirements. Eighty-one percent of respondents were incorrect or unsure of how to log cases as a student in postgraduate year 4. Only 5% of respondents correctly answered all six case logging scenarios correctly based on the current/provided Accreditation Council for Graduate Medical Education guidelines, and only two trainees (1.5%) answered all graduation requirement questions correctly. No trainees answered all case logging scenarios and graduation requirement questions correctly. Having any type of instruction on how to log cases, whether from faculty or peers, did not affect ability to answer case logging scenarios or graduation requirement questions significantly.ConclusionsThere is significant variability when it comes to the case logging habits of IVRs. The majority of trainees do not receive formal instruction on how to log cases. The majority of trainees are also unaware of their graduation requirements as they pertain to the cases the are required to log.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".