Bibliographic record
Abstract
Determining knowledge of accurate population prevalence of rheumatic diseases is important in assessing the burden of the illness in the community and provides a basis for healthcare provision, policy, and workforce planning. The use of self-report is an integral part of determining the population prevalence of many chronic and non-registry–based diseases. Indeed, this is often the only way to obtain prevalence information for these conditions because definitive diagnostic tests may not exist or may be impractical to administer across a large number of people. Both prevalent and incident disease can be determined in this manner; however, there is evidence of a difference in the sensitivity of self-reporting prevalent and incident disease, and differences according to the disease examined. Oksanen, et al 1 determined that the identification of true negatives was equally high for both prevalent and incident disease when compared with national registry data, but the sensitivity of incident ranged from 55% to 63% compared with prevalent disease (78%–96%) for hypertension, diabetes, asthma, coronary heart disease, and rheumatoid arthritis (RA). Both prevalent and incident self-reported diabetes have also been shown over time by Schneider, et al 2 to have 84%–97% specificity and 55%–80% sensitivity when compared with reference definitions (glucose and medication criteria). The prevalence and incidence of inflammatory rheumatic conditions, in particular, is also often only measured using self-reported information, and because of the heterogeneity of diseases within this group, the information may or may not be supplemented and validated by medication data or other relevant clinical tests. A combination of self-report and other forms of … Address correspondence to Dr. C.L. Hill, The Queen Elizabeth Hospital, Rheumatology, 28 Woodville Road, Woodville, South Australia 5011, Australia. E-mail: Catherine.Hill{at}sa.gov.au
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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 | 0.040 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".