The effect of cooling on coagulation and haemostasis: Should “Ice” be part of treatment of acute haemarthrosis in haemophilia?
Bibliographic record
Abstract
Repeated haemarthroses and the consequences of blood in the joint contribute to blood induced joint disease (BIJD) in people with haemophilia (PWH). Prevention of bleeding, through medical management, is the standard of care in developed countries, but is not universally available due to financial and other barriers. Ice application, as part of R.I.C.E. (Rest, Ice, Compression, Elevation) or alone, is commonly recommended as an adjunct treatment to decrease bleeding, pain, tissue metabolism, oedema, and inflammation. This article will review evidence regarding local cooling by commonly used ice application methods, to decrease the temperature of the skin and intra-articular (IA) joint space and the resultant effects on haemostasis and coagulation. The general literature was reviewed for articles in English describing temperatures achievable in the skin and IA space using clinically relevant ice protocols, and the effect of cooling on haemostasis and coagulation. The literature demonstrates that typical methods of ice application can cool both the skin and IA space. Published, general literature studies have also consistently demonstrated that experimental cooling of blood and/or tissue, both in vitro and in vivo in humans and in animal models, can significantly impair coagulation and prolong bleeding. In PWH with acute haemarthrosis, ice application has potential to increase haemorrhage morbidity by further impairing coagulation and haemostasis. Ice has not been shown to improve overall outcome, stop bleeding nor swelling from haemarthrosis. Although ice can help manage acute, haemarthrosis-related pain, there are other available interventions that will not impair coagulation and haemostasis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".