Global haemostasis and point of care testing
Bibliographic record
Abstract
The evaluation of the coagulation profile has used so far either clotting-based or chromogenic assays with different endpoints. Clotting-based techniques are the most used worldwide, and they certainly are useful for diagnosis of clotting factor deficiencies. However, the information provided is relatively limited, and therefore the individual profile of coagulation is poorly assessed. This is reflected by the weak correlation between the results of these assays and the clinical phenotype. Among the assays that benefited from technological advances, thrombin generation and thromboelastography are probably the most actively investigated, but they require specific instruments and are not fully automated. Their standardisation level is rapidly progressing, and they are progressively entering the clinical scene, with the attempt to provide additional information on the coagulation process and a meaningful clinical correlation. These inherited bleeding disorders frequently require replacement therapy using clotting factor concentrates that increase the plasma level of the missing clotting factor. The classical adjustment of the therapy is mainly based on the measurement of the plasma clotting activity of the protein administered. If one considers that a certain level of thrombin generated would predict clinical efficacy, monitoring of thrombin formation might offer new possibilities to individually predict the bleeding phenotype, select the most adapted therapeutic product and tailor the dose. The same holds true for thromboelastography/thromboelastometry which evaluate fibrin formation as well as clot resistance to fibrinolytic challenge, one step further down in the coagulation process. In this regard, these 2 assays could be seen as complementary in terms of information provided on the coagulation profile at the individual level.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".