Bibliographic record
Abstract
Letters to the Editor 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: I work in multiple EDs in our hospital system, including small community EDs, and I commiserate with Sandra Scott Simons, MD, about transfer woes. (EMN. 2021;43[7]:8; https://bit.ly/3dACBBN.) I would add one additional burden of the transfer process. Receiving hospitals are always at or near capacity, and transferring inpatients who decompensate to a higher-level facility is difficult because they typically aren't “allowed” to go to an ED if they are already inpatients. Every hospitalist has a horror story about a patient circling the drain while they spend hours or days trying to get them to an appropriate hospital. The hospitalists' response is to decline admissions “just in case” some low-likelihood possibility happens to the patient, resulting in numerous unnecessary transfers. If lower-resource hospitals could more quickly and easily transfer patients out only if their condition changes, they could keep more patients locally, and perhaps reduce the burden on the higher-resource hospitals, allowing easier transfers. Telemedicine consultation by specialists unavailable at a hospital could also alleviate the need for many transfers as well. Erik Deede, MD Sudbury, MA
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".