Bibliographic record
Abstract
Medical simulation has actually been around a very long time.I still remember my first encounter with a ''high fidelity'' simulation back when I was a newbie in medical education.It was developed by Bill Harless (Harless et al. 1971), with a very large NIH grant.It ran on a mainframe somewhere (there were only mainframes in those days).The simulation began with a colour video of the patient walking down a beach in southern California, and ended with her dying of a heart attack.Well, not exactly.That was one possible ending, which was decided by the digital grim reaper, a random number generator somewhere in the bowels of the computer.In between, the candidate could ask the computer any question she wanted by typing it in, in ordinary language, and the patient would respond appropriately (well, most of the time).From today's vantage, this seems pretty well routine.But in 1971, many of us didn't yet own a colour TV, let alone a video recorder that was computer controlled.Computers occupied large buildings, and you interacted with them by handing in your boxes of cards at the window and picking up your newsprint output a day later at the outbox.To put it in perspective, the computer on board Apollo 13 at about the same time had 2 KB of RAM, and ran at a CPU time of 1 microsecond.My laptop has 1,000,000 KB, and runs at a CPU time of less than a nanosecond.Unfortunately, CASE was too clever for its own good.I once heard that it cost $50,000 to produce a single case.Harless et al. (1971) reported that the author, even after training, had to invest over 6 h just to input the necessary data.Moreover, the technological demands made it inaccessible for educational purposes.No institution could afford a single CASE setup, let alone a computer learning lab (even if one existed, which it did not).As we all know, technological innovations have occurred at lightning pace, so that such a system would now be viewed as a straightforward ''virtual patient'' application, not even beginning to stretch the capacity of the average laptop.But our pedagogical understanding of the value of simulation has not proceeded apace.We continue our love affair with realistic, ''high fidelity'' simulation, and readily accept that the more authentic (and more G.
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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".