Argentine Patagonia: prevalence and clinical features of multiple sclerosis
Bibliographic record
Abstract
There are few studies reporting multiple sclerosis prevalence rates in the Buenos Aires region, Argentina (latitude 34 degrees S) (between 12-18.5/100 000 inhabitants), and no studies have been performed in the larger region between parallels 36 degrees and 55 degrees S. The aim of this study is to determine the prevalence rates and clinical features of multiple sclerosis in residents of the Argentine Patagonia. Four cities from the region were selected for this study, giving a sample population of 417 666 inhabitants (approximately 24% of the total Patagonia population). 1(st) March 2002 was determined as prevalence day. Patients were ascertained using multiple case-finding methods. The point prevalence rate was 17.2/100 000 (17.2 age-adjusted to the world population). Prevalence rates were higher for women than for men, 22.1 versus 12.2/100 000 inhabitants (21.4 versus 12.7 sex-adjusted to the world population). The study population was mainly of European descent and mestizoes. Clinical features were similar to those reported in other countries. This study shows that Argentine Patagonia is a medium-risk area with no south-north gradient between parallels 55 degrees and 36 degrees S. The Patagonia population shows recent internal migration that makes it difficult to determine whether the exposure to potential risk factors has been long enough to modify the disease incidence.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".