Can J Infect Dis Med Microbiol Vol 25 No 4 July/August 2014194
Bibliographic record
Abstract
A 74-year-old woman presented to the emergency department after blood cultures drawn by her family physician four days previously grew Gram-negative bacilli. She had recently returned from a three-month trip to Afghanistan to visit friends and relatives, and admitted to “eat-ing like a local ” while there. During the final two weeks of her stay in Afghanistan, she experi-enced fevers, chills, malaise, night sweats, myalgias and diffuse abdom-inal pain. She was admitted to hospital there and received two weeks of an unspecified intravenous antibiotic without clinical amelioration. On returning to Canada, her symptoms persisted for a third week and, despite multiple clinical assessments, no diagnosis was established. While originally born in Afghanistan, the patient had lived in Canada for nearly two decades. She had not taken many dietary pre-cautions during her trip, and admitted to consuming both cow’s and goat’s milk. Furthermore, she denied any exposure to animals or
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.048 |
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".