Bibliographic record
Abstract
Editor: I read with considerable amusement Dr. Andy Mayer's article sounding the alarm about APPs encroaching into his turf. (“AAEM Opposes NP and PA Independent Practice,” EMN 2019;41[4]:2; http://bit.ly/2Ka5XuP.) Perhaps I would share even a modicum of empathy for AAEM's concerns if I were not one of the many physicians whose (30-year) career was made considerably more difficult as a result of the same alarm he is trying to spread now, which was previously about physicians who trained in a specialty related to emergency medicine and had the audacity to practice EM. I even did post-graduate training. Not pure enough, according to the self-proclaimed guardians of EM, Dr. Mayer's AAEM. Dr. Mayer tried every argument except the only one that counts: Show the data that demonstrate APPs, especially those with the doctorates he fears (lions and tigers and bears, oh, my!), or physicians who trained in a related specialty and then got EM experience provide inferior care! As Dr. Mayer and AAEM know, the data would demonstrate superior care and patient satisfaction. And what do they make of the Canadian experiment of 45 years in which the vast majority of EPs do EM fellowship instead of EM residency and provide care at a much lower cost? Dr. Mayer's concerns have never been and are not now about patient care quality. The AAEM bed was made some time ago. Time to sleep in it. Geoffrey L. Ruben, MD Washington, PA
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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.049 | 0.033 |
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".