Additional file 5 of A population-based study to estimate survival and standardized mortality of tuberous sclerosis complex (TSC) in Taiwan
Bibliographic record
Abstract
Additional file 5. Fig. S2. Map of regional or national prevalence of Tuberous Sclerosis Complex in available literature. Data may represent only regional values in a nation. Data may be estimates according to clinical- or population-based methodology. Region and source of data include: Taiwan (National, population-based using health insurance database, 1/63, 290), Hong Kong (Regional, clinical-based, 1/170, 000 [30]; regional, population-based using hospital administration database, 1/25, 833 [11]), Japan (Regional in San-in, clinical-based, 1/31, 000 [22]), United Kingdom (Regional in western Scotland, 1/27, 000 [23]; southern England, 1/26, 500 [24]; the Oxford region, 1/29, 990 [25]; and Northern Ireland, 1/25, 000 [26]; all clinical-based), Sweden (Regional in western Sweden, clinical-based, 1/12, 900 [27]; national, population-based using health insurance registry, 1/18, 587 [33]), the United States (Regional in Olmsted county, Minnesota, 1/14, 490 [28]; and Rochester, Minnesota, 1/9, 434 [29]; both clinical-based), Canada (Provincial in Quebec, population-based using health-care database, 1/7, 872 [34]), and Germany (National, population-based using surveys sent to pediatric clinics and TSC centers, 1/11, 180 to 22, 360 live births [31, 32]).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.607 | 0.035 |
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".