Diagnosing active tuberculosis in primary care
Bibliographic record
Abstract
### What you need to know A 58 year old transportation worker in India with uncontrolled diabetes (HbA1c 97 mmol/mol) presents with a three month history of productive cough and decreased energy level. He has been treated empirically for community acquired pneumonia twice without improvement in symptoms. A 24 year old student who arrived in Canada from the Philippines three years ago presents with a two month history of bilateral multiple enlarged cervical lymph nodes. Of the 10 million people who develop active tuberculosis (TB) disease each year, approximately three million are not identified by national TB care programmes and many are undiagnosed.1 Diagnostic delays are common in both low and high resource settings2 and lead to worse individual outcomes and ongoing transmission.34 Patients often see several healthcare providers before the disease is diagnosed.56 TB most commonly presents with pulmonary involvement, but can present in a number of ways, most commonly lymphadenitis, pleural effusions, and …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".