Paper and thread as media for the frugal detection of urinary tract infections (UTIs)
Bibliographic record
Abstract
Urinary tract infections (UTIs) make up a significant proportion of the global burden of disease in vulnerable groups and tend to substantially impair the quality of life of those affected, making timely detection of UTIs a priority for public health. However, economic and societal barriers drastically reduce accessibility of traditional lab-based testing methods for critical patient groups in low-resource areas, negatively affecting their overall healthcare outcomes. As a result, cellulose-based materials such as paper and thread have garnered significant interest among researchers as substrates for so-called frugal analytical devices which leverage the material's portability and adaptability for facile and reproducible diagnoses of UTIs. Although the field may be only in its infancy, strategies aimed at commercial penetration can appreciably increase access to more healthcare options for at-risk people. In this review, we catalogue recent advances in devices that use cellulose-based materials as the primary housing or medium for UTI detection and chart out trends in the field. We also explore different modalities employed for detection, with particular emphasis on their ability to be ported onto discreet casings such as sanitary products.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".