Sexually Transmitted Infection Among Adolescents Receiving Special Education Services
Bibliographic record
Abstract
BACKGROUND: To estimate the relative risk of sexually transmitted infections (STIs) among children identified as having learning disabilities through the special education system. METHODS: This cross-sectional study used special education data and Medicaid data from Philadelphia, Pennsylvania, for calendar year 2002. The sample comprised 51,234 Medicaid-eligible children, aged 12-17 years, 8015 of whom were receiving special education services. Claims associated with diagnoses of STIs were abstracted, and logistic regression was used to estimate the odds of STI among children in different special education categories. RESULTS: There were 3% of males and 5% of females who were treated for an STI through the Medicaid system in 2002. Among females, those in the mental retardation (MR) category were at greatest risk (6.9%) and those in the emotionally disturbed or "no special education" category at lowest risk (4.9% each). Among males, STIs were most prevalent among those classified as mentally gifted (6.7%) and lowest among those in the MR category (3.0%). In adjusted analyses, males with specific learning disabilities and females with MR or who were academically gifted were at excess risk for STIs. CONCLUSIONS: The finding that children with learning disabilities are at similar or greater risk for contracting STIs as other youth suggests the need to further understand their risk behaviors and the potential need to develop prevention programs specific to their learning needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".