Paving the way for quality assured, decentralised point-of-care testing for infectious disease in primary care - Real world lessons from remote Australia
Bibliographic record
Abstract
INTRODUCTION: Decentralized molecular testing for infectious disease diagnosis at the point-of-care (POC) is critical to address inequities in access to timely, informed health care. The COVID-19 pandemic accelerated the demand, development and adoption of POC tests for infectious diseases globally. This has provided opportunities to maximize the individual benefits and public health impact of POC testing, particularly in remote and resource-limited primary care settings. Despite this, there remains a lack of harmonized, regulatory compliance and quality management frameworks for the delivery of molecular POC testing networks outside the laboratory setting. AREAS COVERED: This Perspective describes real-world lessons and experiences of delivering a fit-for-purpose, quality framework for one of the world's largest decentralized molecular POC testing programs for infectious disease across rural and remote Australian communities. Here we detail unique, key considerations to ensure the quality of POC testing in primary health settings with global application. EXPERT OPINION: There is an ethical and public health imperative to provide sustained access to decentralized POC testing for infectious disease in primary care. Genuine partnerships across stakeholders and disciplines are essential to deliver well governed, fit-for-purpose quality management POC testing frameworks and increase equitable access to timely, high-quality person-centered care.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".