Systematic evaluation of evidence on veterinary viscoelastic testing Part 4: Definitions and data reporting
Bibliographic record
Abstract
OBJECTIVE: To systematically examine evidence surrounding definitions and reporting of data for viscoelastic testing in veterinary medicine. DESIGN: Standardized, systematic evaluation of the literature, categorization of relevant articles according to level of evidence and quality, and development of consensus on conclusions for application of the concepts to clinical practice. SETTING: Academic and referral veterinary medical centers. RESULTS: Databases searched included Medline, CAB abstracts, and Google Scholar. CONCLUSIONS: All 4 standard thromboelastography (TEG) and rotational thromboelastometry (ROTEM) variables should be universally reported, and the reporting of shear elastic modulus in addition to maximum amplitude (MA) is encouraged. There is insufficient evidence to support universal usage of the coagulation index at this time. The K value and clot formation time are the most variable of the 4 parameters, with alpha angle, MA, and maximum clot firmness generally the least variable. Individual studies should report sufficient data on patients and institutional controls to enable definitions of hypo- and hypercoagulability to be evaluated post-hoc, and it is recommended that all studies specifically report how these conditions were defined. In reporting data relating to fibrinolysis, the TEG variables LY30, LY60, CL30, CL60, and the ROTEM variables LI30, LI60, ML, LOT, and LT should be documented. Studies should report sufficient data on patients and controls to enable definitions of hyper- and hypofibrinolysis to be evaluated post-hoc, and we suggest that standard TEG/ROTEM assays may be unable to detect hypofibrinolysis in companion animals. We recommend that every center establish reference intervals, which are specific to either TEG or ROTEM. These reference intervals should be established using veterinary clinical pathology guidelines, standardized protocols, and a minimum of 40 healthy animals. There are currently insufficient data in companion animals to suggest a utility for Vcurve variables beyond that of standard TEG variables.
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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 | 0.160 | 0.531 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.042 | 0.029 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".