Donald Pinkel on Simoneʼs OncOpinion: ‘The Universityʼs (and Medical Schoolʼs?) Crisis of Purposes’ (10/25/09 issue)
Bibliographic record
Abstract
Joe Simone's essay in the Oct. 25th issue hits the nail on the head. My students are being turned down for medical school despite high grades and excellent recommendations and when interviewed find that up to $70,000 a year is needed. It is a similar situation with nursing schools, which are also turning away qualified applicants despite the severe shortages of nurses, which is expected to become even worse with the aging population. Unless we quickly increase medical school and nursing school capacities, it doesn't matter how or when we extend “health insurance.” This reminds me of a long talk I had with Ken Endicott when he became director of the NIH's medical education subsidy program when Nixon was President. Endicott's idea was that universal health care would be possible only if surgical and medical specialty fees were reduced. By increasing MD output, competition would result, driving them down so universal care became affordable. I also learned at MCW [Medical College of Wisconsin] that the school had to agree that one half of the graduates would go into primary care to receive federal capitation subsidiaries, so that school reshaped its curriculum. In Pediatrics, I reviewed a federal grant to establish and finance a primary care track that paid the residents on it as well as a full-time faculty member. As I recall, much or most of this went out during Reagan's time. But it is surely needed today! Another problem is the high costs of medical and even nursing school attendance. Back in my time, a student could get through med school living at home, taking the bus, and working part-time. Many from working class ethnic neighborhoods got MDs, and after one year of internship returned to their neighborhoods and served their ethnic kin with great pride, if not with much income (they never turned away patients!). Today, more than 90% of med student are from the top 10% economically. How many of the 90% are aware of the lives and needs of the other 90%? Or do many just want to stay in the 10% or even 5% or 1%? The Ontario Health Plan is the way to go, in my view. My relatives there are well served by it and resent the falsifications about it in the US media. Some little fixes may be needed but there is no opposition to that plan by any political group. Keep up the good work—We need the Simone Commission! Donald Pinkel, MD San Luis Obispo, California [Dr. Pinkel was the first Director of St. Jude Children's Research Hospital and recruited Joe Simone there in 1967. Now retired, he also teaches undergraduate students part-time.] Reply from Dr. Simone: Many thanks for your letter. You covered a lot of ground, you make some excellent points, and I am in basic agreement with your assessments.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.056 | 0.027 |
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".