Bibliographic record
Abstract
I try to touch base with the clinic nurses each day before patients and physicians (in that order) start arriving and the usual busyness of the day takes over. A recent early morning conversation centered on one nurse's frustration with the spouse of a man who had been treated in another city and who was now under the care of a physician at our clinic. The woman had been calling the nurse repeatedly since her husband had returned home after the procedure. He was having complications, and she wanted something to be done about this immediately. The nurse was receiving text and email messages from the physician, who was also being contacted by the man's spouse, and his frustration was doubling up the effect on the nurse. The physician had agreed to provide follow-up care for this man and, frankly, I was somewhat surprised at the fuss this was creating.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".