transmitted infection in Hong Kong
Bibliographic record
Abstract
Background: The incidence and prevalence of sexually transmitted infection (STI) in China has been on the rise in the past decade. Delineation of epidemiologic pattern is often hampered by its uneven distribution. Spatial distribution is often a neglected aspect of STI research, the description of which may enhance epidemiologic surveillance and inform service development. Methods: Over a one month-period, all first time attendees of 6 public STI clinics in Hong Kong were interviewed before clinical consultation using a standard questionnaire to assess their demographic, clinical and behavioural characteristics. A GIS (geographic information system)-based approach was adopted with mapping performed. The cases attending the clinics in different locations were profiled. A comparison was made between neighbourhood cases (patients living near a clinic) and distant cases (those farther off), by calculating the odds ratio for demographic, behavioural and geographic characteristics. Results: Of the 1142 STI patients evaluated, the residence locations of 1029 (90.1%) could be geocoded, of which 95.6 % were ethnic Chinese and 63.4 % male. Geographically only about a quarter lived in the same district as the clinic. STI patients aged 55 or above were more likely to be living in the vicinity of the clinic, located in the same or adjacent tertiary planning unit (a small geographic unit below district level). A majority of patients came from
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".