Subcutaneous protein C concentrate in the management of severe protein C deficiency – experience from 12 centres
Bibliographic record
Abstract
Since the first description of subcutaneous protein C concentrate as treatment for severe protein C deficiency in 1996, further cases have been reported but there is no uniform approach to this form of treatment. In order to assess the safety and effectiveness of subcutaneous protein C concentrate and suggest recommendations for future use, patients who had received subcutaneous protein C concentrate were identified from the literature, by contacting the manufacturers and by personal communication. Treatment details were available from 14 cases. Apart from one case where the infusion interval was inadvertently increased, no thrombotic events occurred even when doses were subsequently reduced. Initially, a trough protein C level of >0·25 iu/ml should be aimed for. Subsequently, a smaller dose of subcutaneous protein C concentrate, especially if taken with an oral anticoagulant, may be protective maintenance treatment. The treatment was well tolerated with few side effects. Subcutaneous protein C concentrate on its own or combined with an oral anticoagulant appears to be safe and effective as maintenance treatment of severe protein C deficiency. A major advantage is the avoidance of central venous access devices. The incidence of neurodevelopmental handicap was high with blindness affecting the majority of patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".