Portable devices and mobile instruments for infectious diseases point-of-care testing
Bibliographic record
Abstract
INTRODUCTION: Rapidity, simplicity, and portability are highly desirable characteristics of tests and devices designed for performing diagnostics at the point of care (POC), either near patients managed in healthcare facilities or to offer bioanalytical alternatives in external settings. By reducing the turnaround time of the diagnostic cycle, POC diagnostics can reduce the dissemination, morbidity, and mortality of infectious diseases and provide tools to control the global threat of antimicrobial resistance. Areas covered: A literature search of PubMed and Google Scholar, and extensive mining of specialized publications, Internet resources, and manufacturers' websites have been used to organize and write this overview of the challenges and requirements associated with the development of portable sample-to-answer diagnostics, and showcase relevant examples of handheld devices, portable instruments, and less mobile systems which may or could be operated at POC. Expert commentary: Rapid (<1 h) diagnostics can contribute to control infectious diseases and antimicrobial resistant pathogens. Portable devices or instruments enabling sample-to-answer bioanalysis can provide rapid, robust, and reproducible testing at the POC or close from it. Beyond testing, to realize some promises of personalized/precision medicine, it will be critical to connect instruments to healthcare data management systems, to efficiently link decentralized testing results to the electronic medical record of patients.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".