Approaching the Issue of the Aging Physician Population
Bibliographic record
Abstract
In November 2011, the Coalition for Physician Enhancement (CPE) and the University of California, San Diego, Physician Assessment and Clinical Education (PACE) Program held a conference on the issue of physician aging and its potential impact on clinical performance and quality of care. Speakers and attendees from the United States and Canada reviewed a variety of topics and trends related to aging. Data reviewed during the conference reveal that average physician age is increasing, and while a variety of positive aspects of aging can provide a professional benefit, some studies associate a decrease in physician performance with increasing age. Among the factors that can affect physician performance include solo practice, lack of American Board of Medical Specialties (ABMS) Board Certification, practicing outside the scope of training, high clinical volume and health issues. Conference attendees examined Canadian experiences with age-based competency screening and participated in a survey of opinion regarding age-based screening. The majority favored age-based screening beginning at the age of 70, using a system that would include assessments of physical and mental health and a cognitive screen. Competency screening could include peer review and practice evaluation methods. The authors propose further study of age-based screening and encourage physicians to think carefully about the timing of appropriate modifications to and retirement from practice.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".