Universal Immunization: The diphtheria control strategy of choice
Bibliographic record
Abstract
During the 1993±1997 diphtheria epidemic in Tajikistan, the incidence rate was the highest observed throughout the Newly Independent States of the former Soviet Union (76.2 cases/ 100,000 population in 1995). Factors that contributed to this situation included an increase in the number of persons who were not fully immunized, a breakdown of health care services and disease surveillance, civil war, an increase in migration, shortages of quali®ed medical personnel, and shortages of products, resources, and services. The Ministry of Health and numerous international organizations have worked to address the needs of the republic, and in the fourth quarter of 1995, the number of reported cases began to decrease. It is believed that this decrease was largely the result of routine immunization, implementation of national immunization days, and use of a World Health Organization±recommended system for work-ing with patients and contacts, and it underscores the importance of universal diphtheria immunization with special booster doses in such an epidemic setting. Tajikistan, a republic on the southern tier of the for-mer Soviet Union, has current population estimates of 4,778,000±5,916,000 [1]. In recent years, it has been the focus of much international attention. Wracked by civil war, this republic has experienced the highest reported rates of diphtheria observed in the epidemic that swept across the Newly Inde-pendent States (NIS) of the former Soviet Union [2]. Like other NIS countries, Tajikistan is organized into administrative units called oblasts, which in turn are subdivided into raions. For infectious disease reporting purposes, there are four geographic areas that function as oblasts (viloyatho, singularÐviloyat): (1
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".