Validation of a commercial human ELISA to measure hyaluronic acid concentration in feline plasma
Bibliographic record
Abstract
Our goal was to validate a human hyaluronic acid (HA) ELISA (Hyaluronic acid plus ELISA; TECOmedical Group) for use in feline plasma. Plasma from 5 healthy cats and 5 critically ill cats was used for validation of the assay. Validation methods performed included intra- and inter-assay variability, spike-and-recovery, and dilutional linearity. All measurements were performed in duplicate. The precision study revealed good intra-assay CV of 7.4-8.9%; inter-assay CV was 3.4-4.2%. Extraction efficiency via spiking tests yielded mean recovery of 89.6%. The assay met criteria for acceptable linearity using 3 serial dilutions. Our results demonstrate that this commercial HA ELISA had acceptable analytical performance using feline plasma and could be a useful tool in the veterinary clinical research setting.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".