Patient centric blood sampling and analysis for diagnostics and laboratory medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Blood sampling and diagnostic laboratory analysis are important aspects of our healthcare systems and patient management. However, the process by which the majority of blood specimens are currently collected, venipuncture, does not put the needs of the patient at the center of the process. This article explores the potential utilization of patient centric sampling (PCS) for the collection of smaller blood volumes using technologies that can enable this sampling to take place at a time and location that is more comfortable and convenient for the patient, including self-sampling at home. We discuss the benefits of these technologies, where they are currently used (including case studies), what to consider when contemplating their use and the current regulatory environment. We then explore why the routine adoption of these technologies has been relatively slow and how this impasse may be overcome for the benefit of all patients. This article describes a viable alternative approach for the collection of diagnostic specimens that puts the requirements of the patient at the center. It provides an invaluable resource for those interested in learning about and potentially implementing this approach into their workflows and addresses the concerns that individuals and organizations may have when doing so.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it