Epidemiologic studies in the United
Bibliographic record
Abstract
States (U.S.) have shown that the greatest proportionate increase in AIDS diagnoses over the last decade has been among women.1 The great majority (84%) occur in the 15 to 44 year age group and hetero-sexual contact represents the most rapidly increasing transmission category for women.2 The highest rate of AIDS in U.S. women is found in the Northeast region with most cases occurring in urban areas.3 The proportion of AIDS cases in women is less in Canada than in the U.S. (7.4% versus 18 % for 1994) but has been rising steadily since 1990.4 In Nova Scotia (NS), 8.7 % of AIDS diagnoses have been in women (Table I) and both the number and proportion of new female cases per year has remained stable throughout the epidemic (D. MacDonald, Department of Health, NS: personal communication). AIDS surveillance data is recognized as an incomplete reflection of the true magni-tude of the epidemic. Since the late 1980s anonymous, unlinked seroprevalence sur-veys have been conducted to ascertain in as unbiased a fashion as possible the extent of human immunodeficiency virus (HIV) infection in a given geographic location and/or population group.5 Recent American data indicate that from 1989-1993, the annual prevalence of HIV infec-tion among childbearing women remained relatively stable at 1.6-1.7/1000.2 Several seroprevalence studies in childbearing women have been conducted in Canada with rates varying widely from 0.72/10,000 in Manitoba6 to 8.7/10,000 in Newfoundland.7 This report presents the results of the first anonymous, unlinked HIV seroprevalence survey in NS childbearing women. METHODS
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".