Bibliographic record
Abstract
In Germany, as in the entire WHO Region Europe, the goal has been set to eliminate measles by the year 2007. In order to achieve this, high vaccination rates of >/= 95 % are necessary as well as an intensive surveillance consisting of a continuous record of the age and region-specific incidence, vaccination rates and the seroprevalence. Data on the vaccination status, recorded in the districts at the time of school entry, are collected centrally and evaluated at the RKI since 1998. The vaccination rate for the 1(st) dose against measles is on the average 84.6 % and for the 2(nd) dose 14.3 %. A nationwide sentinel established in 1999 with over 1200 medical practitioners permits an estimate of the country-wide measles incidence on the basis of the registered measles cases. Results from the year 2000 showed a very big difference between the old Federal States (mean incidence of 46.8 illness per 100 000) and the new Federal States (0.9 per 100 000). Most of those falling sick were unvaccinated (85.4 %), where 35 % refused the vaccination. Thirty-nine percent of the cases were examined in the laboratory and from these 58 % were clinically confirmed; 10 % of the cases occurred in those receiving one dose of vaccine and none in those receiving 2 doses. The age peak of the sick children was between 1 to 4 years of age. Measles notification is obligatory in Germany since January 2001. From these data, the incidence of the 1(st) quarter has been calculated. It varies widely depending on the State (e. g. Bavaria 5.7 / 100.000 vs < 0.5 % in new Federal States). The seroprevalance studies conducted in 1993 and in 1995/96 show large gaps in immunity of small children and suggests that the MMR vaccination may have been carried out too late. The elimination of measles in Germany can only be achieved if the vaccination rates are increased and the 1(st) MMR vaccination is performed as early as possible. In particular, the vaccination rates for the 2(nd) dose are inadequate to be able to reach those with vaccine failures in a greater proportion and to close the gaps of vaccination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".