Bibliographic record
Abstract
BACKGROUND: Use of estimated numbers of tuberculosis (TB) cases for planning purposes in some sub-Saharan countries. OBJECTIVE: To document the uncertainties of official World Health Organization estimates and problems encountered in using them for planning. DESIGN: Brief review of the methods used in estimation, using different sub-Saharan countries to illustrate problems. RESULTS: The annual risk of tuberculous infection, used for many years to calculate estimates, is no longer considered a valid method. New methods are based on an assessment of the completeness of TB notification data (the Onion Model) and prevalence surveys of bacteriologically proven pulmonary TB cases; however, these are subject to bias and are very imprecise. Examples from sub-Saharan countries reflect these difficulties and show that official estimates vary substantially, by up to a quarter of the initial values. Donors, particularly the Global Fund, rely on these estimates and push countries to arbitrarily increase planned numbers of notified cases to improve 'case detection rates'. CONCLUSION: Use of estimated numbers to monitor progress in TB control may be counterproductive, costly and risky. It would be much more realistic to accept that low-income countries plan their strategies based on TB notifications rather than on case detection rates that are more dream than reality.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".