Bibliographic record
Abstract
Charlotte, North Carolina. iegilmour@yahoo.comTessler et al. are to be congratulated for the innovative manner in which they have attempted to assess the effect of aging on physician competence, something about which many of us have concerns as we, and our colleagues, age.1However, I am somewhat surprised that in neither the article itself nor the accompanying editorial2is there mention of the significant differences in anesthesia practice between the United States and Canada or of the possibility that these differences might affect the authors’ linkage of the aging of anesthesiologists with both the frequency of litigation and the severity of patient injury related to such lawsuits if applied to the U.S. practice model.In Canada, except in teaching hospitals, anesthesia is given by personal administration, most often by anesthesiologists, but also, in rural hospitals, by specially trained family physicians. In the United States, the supervisory model is used most often. Applying the metaphor in Warner’s editorial, we could say that in Canada older anesthesiologists are “in the driver’s seat,” whereas in the United States, they usually are “backseat drivers”– involved in the crucial parts of the anesthetic but otherwise leaving patient care to the individual actually “behind the wheel.” This difference in practice could affect the applicability of the findings of Tessler et al. to anesthesia practice in the United States, where the age and skills of the anesthesiologist are only part of the equation – where the experience and knowledge of the older anesthesiologist might well be of more consequence than his/her decreased attention span, possible visual/hearing impairment, longer reaction and processing times, or other factors that could be related to the increased “crash rates” of older physicians cited in the study.As noted both by Tessler et al. and by Warner, there is sufficient research on this topic to establish that physicians do not age like fine wines.3,4However, especially in the absence of information as to what actions (or lack thereof) by the anesthesiologists involved lead to the lawsuits, this study is just the first step. As both Tessler et al. and Warner conclude, further research is essential – research based on the supervisory practice model that will help us determine just how and to what extent the observed correlation between anesthesiologist age and patient outcomes applies to practice in the United States., Charlotte, North Carolina. iegilmour@yahoo.com
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".