Quantifying Disease in Challenging Conditions: Incidence and Prevalence of Rheumatoid Arthritis
Bibliographic record
Abstract
Prevalence and incidence are both key measures in decision-making processes and in healthcare management in general. While prevalence informs about the probability of being ill, incidence is related to the probability of becoming sick: Both are very relevant estimates of the frequency of a disease. Available data on the prevalence and incidence of rheumatoid arthritis (RA) display high variability among different geographic areas and over time1,2,3,4,5,6,7. This variability cannot be explained only by genetic factors; other environmental and epigenetic conditions may also influence these figures. Besides, some methodological aspects in the determination of these frequencies might have a strong influence on the informed rates. The annual incidence rates of RA range from 20 to 50 per 100,000 inhabitants in North American and Northern European countries8,9,10,11, while in Southern Europe there is a lower occurrence of the disease12,13,14. Besides, incidence seems to be increasing in recent years in some countries after a drop during the last decade of the 20th century. In South America, 1 study carried out in Argentina and published in 2003 reported … Address correspondence to Dr. D. Scublinsky, Faculty of Medicine, University of Buenos Aires, Paraguay 2155, 15th Floor, Ciudad de Buenos Aires, Argentina. E-mail: darioscublinsky{at}yahoo.com.ar
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".