Biologics and Tuberculosis Risk: The Rise and Fall of an Old Disease and Its New Resurgence
Bibliographic record
Abstract
Tuberculosis (TB) is an ancient disease, recognized in Egyptian mummies and defined by Hippocrates as “phthisis” ( φθίσις , Greek for “consumption”)1. Over the centuries, the epidemiology of TB was characterized by a long period of a relatively stable incidence rate of infection, but the crowding of European cities and the Industrial Revolution favored the spread of Mycobacterium tuberculosis , and TB became an epidemic, a devastating disease with a high mortality rate2. The frequency of TB probably reached its peak in the 18th and 19th centuries with an estimated prevalence in Europe of 900 deaths per 100,000 persons3. This impressive flagellum had a strong influence on social life, and famous artists such as poets John Keats, Percy Bysshe Shelley, and Giacomo Leopardi, authors Robert Louis Stevenson, Emily Bronte, Katherine Mansfield, and Edgar Allan Poe, musicians Niccolo Paganini and Frederic Chopin, and sculptor and painter Amedeo Modigliani were all affected. Contemporaneously, TB became a preferred subject in the arts, as witnessed by Edvard Munch’s portrait of his sister Sophie dying of TB and the touching histories of protagonists of lyric opera such as Mimi in Puccini’s La Boheme and Violetta in Verdi’s La Traviata . Over the following decades, public health measures, improvements in microbiology procedures with the isolation of M. tuberculosis by Robert Koch, and the availability of effective therapies led to a reduction in the incidence of this disease. In the United States, there was a 6% yearly progressive decline in the incidence of TB until 1980, followed by a recrudescence of recorded TB cases, with an increase of 20% between 1985 and 1992 owing to the spread of human immunodeficiency virus (HIV)4. Once again, public health …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".