P3.322 Lack of Standardised Reporting and Documentation of Measures Beyond Accuracy Impair Quality of Research: Evidence from Syphilis and HIV Point-Of-Care Diagnostics
Bibliographic record
Abstract
Background HIV and Syphilis collectively infect about 70 million individuals. Both infections have long asymptomatic periods, making timely screening imperative for infection control. While enough evidence exists on diagnostic accuracy measures for point-of-care tests (POCTs), the quality of evidence on measures beyond accuracy is poor. We reviewed evidence on these implementation research outcomes (IROs) and summarised their quality. Method Two reviewers systematically searched 10+ electronic databases for the period: January 1980-September 2012, independently abstracted data and synthesised outcomes narratively. Over 10,000 citations were screened and a final set of 191 studies identified for inclusion. Results Of 191 studies, almost half 46%(n = 127) in HIV and 41% (n = 64) in syphilis, reported IROs. IROs included acceptability, preference, feasibility and impact. Across 16 studies, acceptability measure was reported as proportions, rates, without confidence intervals often without clear definitions. Across 9 studies, preference was reported as proportion, without definitions or comparators. Feasibility metric across 7 studies, was ill-defined and heterogeneously reported as either completion of strategy, or test procedure, often as a statistic without confidence intervals or a definition or a quantifiable metric. Impact measure (n = 13) was best quantified in clinical trials-reported as either time to treatment initiation, or time to receiving a test result, or change in numbers newly infected or screened with a POCT strategy. Unclear definitions of other IROs, lax measurement resulted in deficient documentation and weak quality ratings on STROBE and CONSORT checklists, raising concerns about the quality of the evidence presented. Conclusion Poor reporting of IROs (i.e, feasibility, acceptability, preference) in POCT diagnostics masked evidence and pointed to the need for standardised definitions, quantification and reporting for them. A framework for documenting metrics beyond accuracy and impact is urgently needed to improve evaluation of true benefits of POCT diagnostics in implementation 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 | 0.132 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".