Immunodiagnosis of tuberculosis: new questions, new tools conference 2008
Bibliographic record
Abstract
Human infection with Mycobacterium tuberculosis exists as a spectrum of conditions ranging from asymptomatic infection to active disease. Novel, accurate tuberculosis immunodiagnostics have been introduced over the last decade, but it remains challenging to timely diagnose active disease and to accurately distinguish asymptomatic M. tuberculosis infection from immune memory resulting from a prior infection eradicated by the host response. The conference titled Immunodiagnosis of Tuberculosis: New Questions, New Tools , which was held on September 21-23, 2008 in Virginia Beach, Virginia, United States, brought together basic scientists and clinical experts to discuss recent progress in tuberculosis research and diagnosis. Global analyses of M. tuberculosis biology and the host immune response, with emphasis on systems approaches to the study of host-pathogen interactions, were presented. Moreover, conference participants discussed new tests in the pipeline and reviewed new technologies leading to novel assay formats. The discussion included technologies ranging from simple, inexpensive point-of-care tests to automated molecular platforms for detection of multiple infections based on the “lab on a chip” concept. It was also recognized that the utility of any new diagnostic relies on laboratory capacity, accessibility, costs, and test deployment. The conference included lessons from the field. For example, the application of existing technologies to neglected areas, such as diagnosis in children and HIV + populations, was discussed.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".