Use of portable blood physiology point-of-care devices for basic and applied research on vertebrates: a review
Bibliographic record
Abstract
Non-human vertebrate blood is commonly collected and assayed for a variety of applications, including veterinary diagnostics and physiological research. Small, often non-lethal samples enable the assessment and monitoring of the physiological state and health of the individual. Traditionally, studies that rely on blood physiology have focused on captive animals or, in studies conducted in remote settings, have required the preservation and transport of samples for later analysis. In either situation, large, laboratory-bound equipment and traditional assays and analytical protocols are required. The use of point-of-care (POC) devices to measure various secondary blood physiological parameters, such as metabolites, blood gases and ions, has become increasingly popular recently, due to immediate results and their portability, which allows the freedom to study organisms in the wild. Here, we review the current uses of POC devices and their applicability to basic and applied studies on a variety of non-domesticated species. We located 79 individual studies that focused on non-domesticated vertebrates, including validation and application of POC tools. Studies focused on a wide spectrum of taxa, including mammals, birds and herptiles, although the majority of studies focused on fish, and typical variables measured included blood glucose, lactate and pH. We found that calibrations for species-specific blood physiology values are necessary, because ranges can vary within and among taxa and are sometimes outside the measurable range of the devices. In addition, although POC devices are portable and robust, most require durable cases, they are seldom waterproof/water-resistant, and factors such as humidity and temperature can affect the performance of the device. Overall, most studies concluded that POC devices are suitable alternatives to traditional laboratory devices and eliminate the need for transport of samples; however, there is a need for greater emphasis on rigorous calibration and validation of these units and appreciation of their limitations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".