Evaluation of thrombelastographic platelet‐mapping in healthy cats
Bibliographic record
Abstract
BACKGROUND: Thrombelastography (TEG) permits analysis of clot formation but it is not specific for platelet activity. TEG PlateletMapping (TEG-PM) is a modification of TEG that uses adenosine diphosphate (ADP) and arachidonic acid (AA) as platelet agonists to define the contribution of platelets to clot formation. OBJECTIVES: The objectives of this study were to determine values for TEG-PM in healthy cats and the interassay variation of TEG-PM. METHODS: TEG-PM analysis was performed on blood specimens collected from 12 healthy cats and was repeated using a second blood specimen collected 2 hours later. Maximum amplitudes generated by thrombin (MA(thrombin)), fibrin (MA(fibrin)), ADP-stimulated platelet activity (MA(ADP)), and AA-stimulated platelet activity (MA(AA)) were recorded. RESULTS: Mean ± SD for MA(thrombin) was 51.1 ± 8.5 mm, for MA(fibrin) was 32.3 ± 17.7 mm, for MA(ADP) was 32.3 ± 15.0 mm, and for MA(AA) was 24.5 ± 12.2 mm. Mean MA(ADP) and MA(fibrin) were not significantly different, whereas mean MA(AA) was significantly lower than mean MA(fibrin). Results from the first and second specimens were not significantly different. Correlation between the first and second specimens was moderate for MA(thrombin), MA(fibrin), and MA(ADP), but was poor for MA(AA). A high degree of variability (coefficient of variation 47.7-60.0%) was observed for MA(fibrin), MA(ADP), and MA(AA). CONCLUSIONS: As MA(ADP) and MA(AA) (AA) were the same as or lower than MA(fibrin), a valid baseline to determine platelet-stimulated clot formation could not be established. Considerable interassay variation and wide intervals for MA(fibrin), MA(ADP), and MA(AA) values in this study indicate that TEG-PM should be used cautiously in feline patients. Several preanalytical factors should be examined in further detail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".