Clinical Significance of Cryofibrinogenemia: Possible Pathophysiological Link with Raynaud’s Phenomenon
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical findings and prevalence of patients with cryofibrinogenemia (CF) and to determine whether CF is associated with primary Raynaud's phenomenon. METHODS: Between June 2006 and December 2009, 227 patients were tested for CF in a single university hospital. Forty-five patients with primary Raynaud's phenomenon were tested for CF. RESULTS: A total of 117 patients with CF without cryoglobulinemia were included. The main clinical manifestations included skin manifestations (50%) and arthralgia (35%). There were 67 patients with primary CF and 50 patients with secondary CF. There was no significant difference in the mean concentration of the cryoprecipitate in primary CF as compared to the secondary form (172 ± 18.6 vs 192 ± 20.9 mg/dl, respectively; p = 0.41). Highest concentrations of cryoprecipitate were observed in those containing fibrinogen only as compared to cryoprecipitates containing fibrinogen and fibronectin (301 ± 43.5 vs 125 ± 10.6 mg/dl; p < 0.001). Patients having skin necrosis (n = 3) had significantly higher values of cryofibrinogen compared to those without necrosis (638 ± 105 vs 160 ± 10.2 mg/dl; p = 0.0046). Among the 45 patients with primary Raynaud's phenomenon, 36 had associated CF. There was no significant difference in the mean concentration of the cryoprecipitate in these patients compared to those with primary CF. CONCLUSION: There seems to be a significant correlation between cryofibrinogen concentration and the severity of the clinical signs, particularly when cryoprecipitate is composed of fibrinogen alone. CF might have a possible pathophysiological role in primary Raynaud's phenomenon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".