Challenges and perspectives on the use of mobile laboratories during outbreaks and their use for vaccine evaluation
Bibliographic record
Abstract
Mobile laboratories provide diagnostic capabilities for routine surveillance and patient identification during an outbreak. In either situation, they face many challenges including identification of the appropriate assay(s) to employ, logistical arrangements, and providing for the health and safety of the laboratory staff. Great strides have been made over the last decade in the development of mobile laboratories with assays that require minimal infrastructure and technical experience. This knowledge and expertise have been developed in partnership with many researchers and public health officials who live in regions prone to infectious disease outbreaks. Mobile laboratories should now also be used in the evaluation of novel vaccines and therapeutics in remote locations. Clinical mobile laboratories will include similar diagnostic capabilities as outbreak response mobile labs, but will also include additional point-of-care instruments operated under Good Clinical Practice guidelines. They will also operate rigorous data management plans so that the data collected will satisfy regulatory agencies during the licensure process. Failure to deploy an adequate clinical mobile laboratory when administering a novel biological product in a remote location is a significant limitation to any collected scientific data that could ultimately undermine clinical development and availability of life-saving interventions.
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 | 0.149 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".