Bibliographic record
Abstract
On Being a Doctor1 January 2008Princess AbraPaul Moorehead, MDPaul Moorehead, MDFrom Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3V6, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-148-1-200801010-00011 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Once upon a time, Abra and I met in the emergency room. She had no hair and the side of her head was scarred from surgery, but she was pretty anyway. Soft in her voice and sweet of nature, she was helpful as I examined her, even though she wasn't feeling well. Abra wore her toque elegantly, as though it were a crown.This is the kind of child who gets cancer.Abra had a brain tumor. Most of it had been cut out, and what remained had been irradiated. Now she was being given chemotherapy, a brew of poisons ... Author, Article, and Disclosure InformationAffiliations: From Memorial University of Newfoundland, St. John's, Newfoundland and Labrador A1B 3V6, Canada.Corresponding Author: Paul Moorehead, MD, Children's Hospital of Eastern Ontario, Room 5109, 401 Smyth Road, Ottawa, Ontario K1H 8L1, Canada; e-mail, [email protected]com. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 1 January 2008Volume 148, Issue 1Page: 80KeywordsChemotherapyDrugsEmotionsFeversHereditary nonpolyposis colorectal cancerMorphineShinglesSleepSurgery ePublished: 1 January 2008 Issue Published: 1 January 2008 Copyright & PermissionsCopyright © 2008 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.438 | 0.216 |
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".