Interpretation of laboratory detection trends for<i>Chlamydia trachomatis</i>and<i>Neisseria gonorrhoeae:</i>Manitoba, Canada, 2000–2012
Bibliographic record
Abstract
OBJECTIVES: Increases in case numbers for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) have been noted on a global level. This study analysed 13 years of testing data to better understand case detection trends over time. METHODS: Data consisted of all nucleic acid probe and nucleic acid amplification diagnostic testing for CT and NG for the population of Manitoba, Canada (1.2 million); January 2000 to December 2012. Logistic regression models were used to analyse ORs associated with positive CT and NG tests by year. Included in the model as predictor variables were test type, specimen type, patient age and residence location. RESULTS: For both male and female CT results, unadjusted OR by year mimicked absolute case counts, reflecting a general increase over time in case counts. Adjustment for laboratory-related variables altered this relationship such that a general decline in the odds of identifying a CT case over time was evident. For both male and female NG results, adjustment for laboratory and demographic variables altered the OR associated with each year, but to a lesser extent than for CT. CONCLUSIONS: Temporal trends associated with CT case numbers should be interpreted after controlling, at a minimum, for the influence of laboratory-related variables. Interpretation of NG trends is feasible using only the number of reported NG cases.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".