Letter to the Editor: Thanks to Dr. Leap for Enjoyable Reading
Bibliographic record
Abstract
Emergency Medicine News welcomes letters to the editor about any subject related to emergency medicine. Please limit your letter to 250 words, and include your full name, credentials, and city and state of residence or practice. Letters may be edited for content, length, and grammar. Submission of a letter constitutes the author's permission to publish on all media, including print, online, and social media, but does not guarantee publication. Letters express the views of the authors and do not necessarily reflect those of Emergency Medicine News and Wolters Kluwer. Letters to the editor may be sent to [email protected]. Editor: Yet another wonderful article from Edwin Leap, MD. (EMN. 2024;46[8]:4; https://tinyurl.com/y8pxuxys.) Thank you so much to him for these 25 years of enjoyable reading. His column is always the one I read first. Glad he is holding out to work a bit longer. I'm 77 and retired at 60 after 33 years (of Vicodin begging, EMR learning, ultrasound importance, night shifts.) But then I shifted over to the free clinic. He would like it too. You don't make any money, of course, but people always say thank you, even if it's in Spanish or Russian. And good luck on that novel. Robert Rosenthal, MD Vancouver, WA
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".