Bibliographic record
Abstract
As my career (such as it is) winds its way on, I have begun to consider its impact: have I “given anything back”? Might I, for example, have a Rule named after me (as Canada has done, repeatedly, not that I’m comparing myself with an entire country, really, but how many rules can one country have?) that will teach residents and perhaps attendings in distant programs and indeed hospitals AND countries (like maybe the one to my north)? During my residency I was present at the creation of a rule that was transiently famous at Christiana Hospital, called the Stearn-Scott Hopper Sign. It was named for a resident and an attending in the program at Delaware who have asked me to leave out any further identifiers. That rule stated that any complaint involving a patient over the age of 70 years and a toilet meant that the patient would go to the intensive care unit (ICU) if he or she didn’t die in the emergency department (ED). Likewise, for at least 15 years after I started at Rhode Island Hospital, where (as elsewhere) the ICU bed numbers never seem to keep pace with the numbers of sick ED patients, all one had to say to get a patient admitted to a Unit was “well, they’re Hmong” and beds would mysteriously open up. This was known as the Cambodian Exception: even what seemed like a urinary tract infection could go to the ICU, if the patient was Hmong (a stoic and hospital-averse people, in large part). I think my only claim to originality might be called The Libby Law—a.k.a. the One Car Rule: one complaint comes to the ED in one car or, to explicate more fulsomely, the seriousness of a presentation can be divided by the number of people who travel together in one mode of transport to arrive in your ED. It is self-evident, most of the time—we have all had our expectations of an emergent condition ratcheted significantly downward when the toothache in bed three turns out to be the significant other of the low back pain in bed 10, and that is as it should be; each one has a third of a significant problem, and the apparent math mystery is solved when it is discovered that they both were driven to the ED by the “medication refill” patient in bed 15. In another scenario, the Really Sick Guy in the critical care room was driven to the hospital by the migraine patient in bed 11 (in this case, since the migraine was caused by the stress of the drive with the Really Sick Guy, part of the severity of the headache is subsumed under the critical illness, adding up to the necessary One Complaint …; you see, it all works out). Likewise, the four kids with colds (four folders, all in bed number 5) should, by virtue of the Libby Law, all be colds. Just colds. And not as bad as the one you are currently working with. It’s a modest proposal, and in truth I would not mind (much) if it is just known as the Car Rule. Although then I’ll have to drag a copy of this journal with me, to reinforce my authorship. You will have noticed that Canada does not have to do that.
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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.077 | 0.086 |
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".