Poor Reporting of Outcomes Beyond Accuracy in Point-of-Care Tests for Syphilis: A Call for a Framework
Bibliographic record
Abstract
Background. Point-of-care (POC) diagnostics for syphilis can contribute to epidemic control by offering a timely knowledge of serostatus. Although accuracy data on POC syphilis tests have been widely published, few studies have evaluated broader outcomes beyond accuracy that impact patients and health systems. We comprehensively reviewed evidence and reporting of these implementation research outcomes (IROs), and proposed a framework to improve their quality. Methods. Three reviewers systematically searched 6 electronic databases from 1980 to 2014 for syphilis POC studies reporting IROs. Data were abstracted and findings synthesised narratively. Results. Of 71 studies identified, 38 documented IROs. IROs were subclassified into preference (7), acceptability (15), feasibility (15), barriers and challenges (15), impact (13), and prevalence (23). Using our framework and definitions, a pattern of incomplete documentation, inconsistent definitions, and lack of clarity was identified across all IROs. Conclusion. Although POC screening tests for syphilis were generally favourably evaluated across a range of outcomes, the quality of evidence was compromised by inconsistent definitions, poor methodology, and documentation of outcomes. A framework for standardized reporting of outcomes beyond accuracy was proposed and considered a necessary first step towards an effective implementation of these metrics in POC diagnostics research.
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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 | 0.744 | 0.810 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.022 | 0.049 |
| Open science | 0.016 | 0.019 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".