Bibliographic record
Abstract
Editor: I enjoyed Dr. James Roberts' column, “Now That You're a Real Doctor: Lessons for Years to Come.” (2005;2[10]: 22.) Every word rang true. I would add a particular point, as his introduction solicited. When emergency department nurses politely tell you they are concerned about a particular patient, pay attention. What they really mean is, “Come now!” They have been doing this work for a long time, and there is nothing like experience in emergency medicine. Dr. Roberts' columns always inform me. I put him on a pedestal with a few great teachers from whom I continue to learn despite my (can it really be?) 15 years in practice. Jerome Hoffman, MD, from UCLA and Jeff Mann, MD, from Canada are in the same group. Only last week did I realize that he is the same Dr. Roberts of Clinical Procedures in Emergency Medicine, the most well-worn book in my department. Thanks to Dr. Roberts for such a great text; it has helped my colleagues and me innumerable times. Matthew Perl, MD San Diego, CA
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".