bacteraemia, and Clostridium difficile infection data for England up to April-June 2012
Bibliographic record
Abstract
HPA London has published a review of the epidemiology of tuberculosis in London residents, including characteristics and distribution of cases, trends in anti-tuberculosis drug resistance, clustering of tuberculosis cases and treatment outcomes [1]. The report – based on an analysis of data for 2011 carried out by the Agency's London regional epidemiology unit – finds that London continues to account for the majority of tuberculosis cases in the UK – 39 % of the national total, and has an incidence rate over three times that nationally [2]. The importance of social risk factors as a precursor to, or aggravating factor in, TB infection is highlighted in the report [1]. Of the 3,511 people diagnosed with TB in the capital, one in 10 were known to have a social risk factor including: homelessness, drug or alcohol misuse, imprisonment or mental health issues, with more than a quarter of these patients reporting multiple risk factors. In some areas, this was as many as one in three patients. Cases reporting social risk factors were more likely to have infectious forms of disease, and while levels of drug resistance are high in the capital – approximately one in 10 culture confirmed cases exhibited resistance to one or more first line drugs – this was also more common among those with social risk factors Overall in London, 86 % of TB cases completed treatment within 12 months, exceeding the national and
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".