Bibliographic record
Abstract
This Horizon Scan summarizes available information regarding rapid point-of-care testing for the detection of Treponema pallidum, the bacteria that causes syphilis. Rapid point-of-care testing to screen people for a possible case of syphilis allows health care providers to screen people where they are, rather than relying on people’s access to traditional health care settings. The rapid provision of test results can also help to guide treatment in the moment, rather than requiring additional appointments that could increase the number of people with active syphilis infections lost to follow-up. There are currently no point-of-care syphilis tests licensed for use by Health Canada; however, at least 1 multiplex syphilis and HIV-1/HIV-2 detection test could be licensed for use in Canada by the end of 2022. Based on the evidence reviewed, rapid tests for the detection of syphilis appear to be adequately sensitive and specific for screening. The use of point-of-care testing, at-home self-testing, at-home sample collection methods, and telemedicine and virtual care options may be interventions to consider as health care systems move forward and work to catch up on the screening backlog and missed tests related to the COVID-19 pandemic, and also find ways to connect with people who have previously been harder to reach.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.037 |
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".