Bibliographic record
Abstract
Regarding the article in the April 10 issue about the ASCO study predicting that there will not be enough oncologists to meet the need by the year 2020, all I can say is that the organization has completely missed the point. They may be very good at compiling statistics, but they are not so good at figuring out what they mean. It should come as no surprise to those of us who work for a living that no one wants to be a medical oncologist anymore. The reason is simple: In the wake of the Medicare Modernization Act of 2003, we have taken a 68% decrease in overall profitability. Plainly put, it no longer pays to give chemotherapy. This makes us the lowest-paid internists in town. Who in their right mind would spend an extra three years in Fellowship to enter a profession in which you will work the first 10 years of your practice life to pay back your student loans? In the training program at which I once taught, three out of four Fellows quit over the last two years for this reason. The young people are not stupid. Point #1: Providing guidance and support to help increase ASCO'S members' productivity and efficiency is nice, but as we have discovered, seeing 35 patients per day now, you eventually reach the point beyond which no one can reasonably go. Entering into initiatives with non-physician oncology professionals and general practice physicians is probably a good idea, because general practice physicians and RNPAs are the people who going to be giving cancer care in the near future. The only modification to the oncology Fellowship training programs that you are going to be able to make is getting rid of two-thirds of them for lack of interest. I cannot see how collecting any more data to monitor trends to help prevent shortfalls would be even a worthwhile thing to think about. The trend is for less and less money every year until there are none of us left in the community. What this means, of course, is that medical oncology will be confined to the universities and two or three of the large national groups, and of course, the nature extent and implications of this are already well known. All one has to do is to glance at Japan or Canada for five minutes to figure this one out. As the next round of cuts in reimbursement and increase in operating overhead, which is euphemistically called “Pay for Performance” takes effect, most of us in Small Town, America will be out of business. At that point, access to care is going to diminish logarithmically. At which point, society will have to decide whether it wants cancer care or not. If the answer is yes, then I suggest they pay us. You do the math. David C. Tabor, MD Crossville Medical Oncology Crossville, TN
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.104 |
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".