Pre-Analytic Sources of Error with the Serum PAG-Pregnancy Test for Cows
Bibliographic record
Abstract
The pregnancy associated glycoprotein (PAG) test for pregnancy detection in cows necessitates transportation of blood samples to the laboratory. This investigation addresses preanalytic sources of error that might compromise its reliability. During shipping blood samples undergo substantial temperature fluctuations (Experiment 1). Temperatures of whole blood beyond 0°C had no effect, whereas freezing reduced measurements by 22% at -10 °C and by 25% at -20 °C (Experiment 2). Freezing of blood with low PAG content (Experiment 3) caused an increase from 2.4 to 3.7 ng/ml (P < 0.01). Cryopreservation of serum with various PAG concentrations (Experiment 4) brought about increases to varying degrees. The presence of heparin and EDTA in collecting tubes had no effect on PAG measurements, whereas citrate caused an initial reduction, but remained stable thereafter (Experiment 5). In blood stored six months at chilling temperature no change in PAG values occurred as long as samples contained heparin or EDTA (Experiment 6). In Experiment 7 vortexing of whole blood showed no effect, whereas freezing and dilution with water seriously compromised results. In summary, to obtain reliable PAG measurements, contamination with water must be avoided; freezing of whole blood or serum and the use of collecting tubes containing citrate will result in inaccuracies without altogether distorting results. High ambient temperature, physical agitation and long term storage at chilling temperature in the presence of heparin or EDTA will have no impact. PAG determination in blood may thus be considered a reliable pregnancy test for cows in most situations.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".