Canadian Laboratory Standards for Sexually Transmitted Infections: Best Practice Guidelines
Bibliographic record
Abstract
Sexually transmitted infections (STI) continue to spread, and show no international boundaries. Diseases such as gonorrhea and syphilis, which we thought were under control in Canadian populations, have increased in incidence. Sexually transmitted or associated syndromes such as cervicitis, enteric infections, epididymitis, genital ulcers, sexually related hepatitis, ophthalmia neonatorum, pelvic inflammatory disease, prostatitis and vulvovaginitis present a challenge for the physician to identify the microbial cause, treat the patient and manage contacts. During the past 10 years, new technologies developed for the diagnosis of STIs have provided a clearer understanding of the real accuracy of traditional tests for the diagnosis of infections caused by Chlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, herpes simplex viruses, hepatitis B virus, human papillomaviruses, HIV, Haemophilus ducreyi, Trichomonas vaginalis and mycoplasmas. This has presented a major challenge to the diagnostic laboratory, namely, selecting the most sensitive and specific test matched with the most appropriate specimens to provide meaningful and timely results to facilitate optimal patient care.
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 | 0.055 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.017 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".