© 2000 Canadian Medical Association or its licensors
Bibliographic record
Abstract
A 36-year-old aboriginal woman has a 12-mm-diameter reaction to a tuberculosis (TB) skin test, done as part of a community survey. She was vaccinated with BCG (bacille Cal-mette–Guérin) at 6 weeks of age. There is no documented history of contact with TB, and no skin tests were carried out previously. The woman’s family doctor wonders about the significance of the result. Case 2 A 27-year-old aboriginal man presents with a 6-week history of cough and fever and re-cent onset of hemoptysis. He has recently completed 2 courses of antibiotics, but there has been no improvement in symptoms. Chest radiography shows right upper lobe pneumonia. A further course of antibiotics is prescribed. What investigations should be completed next? Case 3 The mother of a 2-day-old aboriginal infant asks for advice about the benefits of BCG vaccination. She is confused about the side effects of BCG and its effectiveness. Tuberculosis (TB) remains a major public health problem for aboriginalpeople in Canada.1 Although incidence rates for this segment of the popu-lation have declined in many parts of the country,2 they remain unaccept-ably high. These rates tend to be highest in more northerly and remote areas,
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.812 | 0.760 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".