Pulmonary Nontuberculous Mycobacterial Disease, Ontario, Canada, 1998–2010
Bibliographic record
Abstract
We measured the prevalence and temporal trends of pulmonary nontuberculous mycobacterial disease among residents of Ontario, Canada, during 1998-2010.Five-year prevalence increased from 29.3 cases/100,000 persons in 1998-2002 to 41.3/100,000 in 2006-2010 (p<0.0001).Improved laboratory methods did not explain this increase, suggesting a surge in disease prevalence. P ulmonary nontuberculous mycobacterial (pNTM) dis-ease is clinically challenging.Therapy entails complex antimycobacterial drug combinations, typically for 18 months (1), often with poor tolerability (2) and limited success (3).pNTM disease is increasingly common in Canada (4) and the United States (5-7), but its prevalence is not well understood.Determining the epidemiology of pNTM disease is difficult for several reasons.It is generally not reportable, so population-level data are not routinely compiled.The diagnosis requires clinical and radiologic information in addition to microbiological examination (>2 positive sputum cultures or 1 bronchoscopic or biopsy culture) (1).Finally, the chronic nature of pNTM disease dictates longitudinal study, illustrated by considering that only a minority with pNTM disease appear to be treated (18% in 1 study) (6), treatment succeeds in only 56% (3), and disease recurs in >30% of patients (2,8).These data indicate that most pNTM cases are expected to be chronic.Cases detected by isolation of nontuberculous Mycobacterium spp. in 1 year, generally remain prevalent over several subsequent years, regardless of the reliable appearance of subsequent isolates, with a disease duration that may depend primarily on patient survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".