Prevalence of multiple sclerosis in Buenos Aires, Argentina using the capture‐recapture method
Bibliographic record
Abstract
BACKGROUND: Scarce data exist about multiple sclerosis (MS) prevalence in South America. The objective of the study is to determine the prevalence of MS in a high populated area from Argentina (Greater Buenos Aires Metropolitan area) using the capture-recapture methodology. METHODS: Greater Buenos Aires is the generic denomination that refers to the megalopolis comprised by the autonomous city of Buenos Aires and the surrounding conurbation of the province of Buenos Aires. The study was carried out taking July 1996 as the prevalence month. We used capture-recapture method to estimate the prevalence of MS cross matching registries from four MS Centers. RESULTS: A total of 803 registries were obtained from the four lists. Log-linear model for capture-recapture method was used to analyze the data. The population of the area based on the 1990 census was 12,594,974; the number of MS cases estimated amongst sources interactions were between 1833 and 2359; the prevalence estimated ranged from 14 to 19.8 cases per 100,000 inhabitants. CONCLUSIONS: This is the first study to provide epidemiological data on the prevalence of MS in a large population in Argentina (Greater Buenos Aires Metropolitan area). Further epidemiological studies will clarify the true prevalence of MS in South America.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".