Bibliographic record
Abstract
I read with more than routine interest the article by Ted Pincus and his colleagues, "Hotel-Based Medicine" 1 .There are 2 subsets of this genre that deserve comment: putative speaker training and steakhouse medicine.A few months ago, I was invited to a "speaker training session" for a new drug, held, of course, at a fancy hotel.I was amazed to discover that: (1) I was one of more than 50 doctors in the room representing many different specialties; (2) the presenters were on a different floor of the same hotel, but their lectures were being videocast into our room; and (3) the lectures were also being broadcast to hundreds of other doctors in many additional locations.It soon became clear that most of the audience would probably never lecture to anyone about anything, and that this was really a promotional marketing meeting being disguised as speaker training.Although comments about the slides were solicited in order to justify the "consultancy," it seemed quite unlikely that any of the suggested changes would pass review by the lawyers.That was also my first exposure to the concept that the slides would have to be presented to an audience without any alteration.On my way home, I had a Eureka moment.Here on Long Island, most rheumatologists get 4-6 dinner invitations a month for the fanciest places in the area, mostly now for talks about biologics.The representatives for the drug companies are pressured by their managers to fill the room, and they get frustrated when doctors say that they have too many dinners and are tired of the same old speakers on the same old topics with the same old choice of salmon or shell steak.So the solution is obvious.Since the speaker's sole function is to read the slides without comment, the companies should hire attractive movie stars (of either sex) to give the talks.Surely if Angelina Jolie or Brad Pitt were talking about a new treatment for rheumatoid arthritis, the house would be packed.I for sure would gladly sign up!
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.124 | 0.074 |
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".