Bibliographic record
Abstract
CASE “You killed one of our patients” On my first night on call as a third year medical student on internal medicine rotation, a 52-yearold man was brought into the hospital by his son and daughter because of lumbar and bilateral hip pain so severe he could no longer get out of bed. Two months earlier he had gone to his local Emergency Department complaining of a seizurelike episode. His doctors had diagnosed lung cancer with metastasis to his brain and he had undergone surgery to remove his brain tumor. We began to work to alleviate the pain and performed daily musculoskeletal and neurological exams. This went on for about one week; each morning arriving early to check on the vitals and physical. One morning, strangely enough, I could not locate the man's chart. When I walked into his room, he was no longer there. I was annoyed, believing that my patient had been moved to a different floor, thus wasting my preround minutes. When I approached a nurse to ask the whereabouts of my patient, I was shocked to hear that he had passed away overnight. I felt strange. I kept thinking about my daily exams and how pointless they had been. I felt neglected; left out. Though only a student, I felt that I should have been there with the family. As I mulled over these thoughts, our team made our way to morning report.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".