Bibliographic record
Abstract
To the Editor: Although the idea of expanding primary care physicians’ (PCPs’) skills to include some specialty skills1 sounds good and may work well in some areas of rural Canada, it would be difficult and even dangerous for PCPs here in the United States. In fact, the results could be disastrous. First, in primary care in the United States the burden of administrative demands is extraordinarily onerous, and it alone has already brought on burnout in many PCPs. These doctors’ energies and emotional reserves are already stretched to the maximum, and any further strain would be ill conceived and lead to disaster. Second, the U.S. litigation system is extremely aggressive and adversarial. Using PCPs to provide services beyond their usual skill set greatly increases their vulnerability to malpractice suits. If specialists are seeing problems that they feel should be seen by a PCP then they should charge fees similar to what a PCP would charge. That is a better way of controlling costs. Edward Joseph Volpintesta, MD President, Bethel Medical Group, Bethel, Connecticut; [email protected]
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.053 | 0.053 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".