Effect of plasma transfusion on serum amyloid A concentration in healthy neonatal foals and foals with failure of transfer of passive immunity
Bibliographic record
Abstract
BACKGROUND: Anecdotal evidence suggests plasma transfusions increase serum amyloid A (SAA) concentrations in healthy neonatal foals making this marker of inflammation inappropriate for therapeutic decision making in such animals. HYPOTHESIS/OBJECTIVES: Administration of hyperimmune fresh frozen plasma (FFP) increases SAA concentration in healthy foals and in foals with failure of transfer of passive immunity (FTPI). ANIMALS: Eighty-six healthy foals. METHODS: Prospective cohort study. Foals <24 hours of age receiving plasma transfusion for treatment of FTPI (serum immunoglobulin G [IgG] concentrations <8 g/L; n = 17) or as a preventative measure for Rhodococcus equi infection (IgG >8 g/L; n = 33) were enrolled. A healthy nontransfused group of foals (IgG >8 g/L; n = 21) also was included. Serum amyloid A concentration was determined before (t0h) and after (t24h) administration of FFP. Changes in blood SAA concentration were assessed using linear regression models. RESULTS: No statistical differences were found in SAA concentration at t0h or t24h among the 3 groups (P > .05, for all comparisons). The variation in SAA concentration before (t0h) and after (t24h) plasma transfusion showed that administration of FFP was not associated with the changes in SAA concentration (P > .05). An association between SAA concentration at t0h and at 24 hours (P < .05) was identified, where foals with higher SAA concentration at t0h also had higher SAA concentration at t24h. CONCLUSIONS AND CLINICAL IMPORTANCE: Administration of FFP to newborn foals was not associated with changes in SAA concentration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".