Bibliographic record
Abstract
To the Editor: As medical director of a nine-year-old procedure service at one of the nation’s largest academic medical centers, I read the recent article by Vaisman and Cram on procedural competence with great interest.1 The scenario described by these Canadian authors is unfortunately common in U.S. training facilities also. While I would concede that a perceived “drive for efficiency” has resulted in referring procedures that were once within the world of internal medicine to others, I would disagree that it is actually efficient. Subspecialists have requisite knowledge, skills, training, and experience that are rarely necessary to successfully perform a bedside procedure. In fact, shifting such procedures to interventional radiology (IR) may result in unnecessary exposure to radiation, wasteful resource utilization, and slower patient throughput. Further, performing a procedure at the bedside allows that patient to progress through the system while simultaneously allowing IR facilities and personnel to be used more efficiently. We in internal medicine must stop giving away the very procedures that form the underpinning of our specialty. Since a numerical threshold for competency determination has not been demonstrated to be evidence based, we, like the American Board of Internal Medicine, moved away from such at our institution. We previously published criteria to define competency based on patient outcomes, what we think is the ultimate metric.2 We have the benefit of a resident-run, attending-staffed procedure service, but we conduct faculty-level training, mentoring, and proctoring for those interested in refining and improving their skills. In our dedicated paradigm, we directly observe and evaluate procedural performance by residents and are able to provide them immediate feedback. Using our critical skills checklist, we are able to deem residents competent, regardless of the number of procedures performed. Anecdotally, some have argued that these skills have been lost because of time constraints, lack of uniformity in prior training, poor payment, and lack of interest. Those who do not still perform procedures should relinquish such privileges at their institution. However, I believe that bedside procedural skills need to return to the aegis of internal medicine and that those who are like-minded should be retrained. Our program has made great strides in reclaiming such lost territory, having been consulted more than 10,000 times since our 2007 implementation. Joshua D. Lenchus, DO, RPhAssociate professor of clinical medicine, anesthesiology, and radiology, University of Miami Miller School of Medicine, Miami, Florida; [email protected]
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 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 | 0.013 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".