Bibliographic record
Abstract
Thrombus dissolution is dependent on activators of plasminogen, the principal enzyme of fibrinolysis, reaching plasminogen bound to the surface of fibrin, and overcoming the many inhibitors of clot lysis present in the plasma milieu. In dialysis patients with occluded catheters and grafts, three activators - streptokinase, urokinase, and tissue plasminogen activator (tPA) - have been used. Streptokinase has fallen out of favor because of its adverse effect profile; urokinase has been the mainstay of therapy. Urokinase has been used alone and in conjunction with mechanical methods for clearance of thrombi from arteriovenous grafts. It has also been instilled into occluded central venous catheters, but often is more effective if given systemically during dialysis in order to lyse the fibrin sheath that surrounds the catheter tip. Due of manufacturing problems, urokinase is no longer available and management with tPA is being actively investigated. One small trial showed that recombinant tPA was significantly more effective than urokinase for restoring catheter patency, but the drug is not yet approved for this purpose by the FDA, and current packaging is not optimal. The problems with thrombolytic agents may be obviated in the future by better methods of prevention of thrombus formation, monitoring flow to anticipate occlusion, and early mechanical interventions to restore patency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.035 |
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".