Bibliographic record
Abstract
We studied Mycobacterium avium lymphadenopathy in 183 Swedish children (<7 years of age) from 1998 through 2003. Seasonal variation in the frequency of the disease, with a peak in October and a low point in April, suggests cyclic environmental factors. We also found a higher incidence of the disease in children who live close to water. Mycobacterium avium is the most common of the nontuberculous mycobacteria that infect otherwise healthy children (1). It manifests as a chronic granulomatous lymphadenopathy in the neck region that preferably is treated by excision of the affected lymph node (2). In Sweden the incidence of nontuberculous mycobacterial disease in children increased from 0.06/100,000 population to 5.7/100,000 after discontinuation of the general BCG (Bacillus Calmette-Guérin) vaccination program in 1975 (3). M. avium is common in the environment. It has been isolated from natural and human-made water systems as well as from soil and animals (4,5). The role of animals in its epidemiology is not clear. The main hypothesis is that oral contact with M. avium–infected water causes lymphadenitis in the head and neck region (4). An environmental study in nearby Finland found nontuberculous mycobacteria in 100 % of brook water samples (6). Human populations who live close to water have a higher proportion of persons with positive reactions to M. avium and other nontuberculous mycobacterial sensitins (7,8). In Sweden, 25.4 % of non-BCG–vaccinated schoolchildren in an urban coastal area reacted to M. avium sensitin (7). In studies from Canada and the United States/ Canada, M. avium disease in children has previously shown no seasonal variation (9), or it has been dominant in winter, spring, or both (1,2).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".