Diagnosis and Management of Subclavian Vein Thrombosis Occurring in Association with Subclavian Cannulation for Hemodialysis
Bibliographic record
Abstract
With the aim of improving the diagnosis and treatment of subclavian vein thrombosis, associated with subclavian cannulation for hemodialysis, we performed Doppler examination of the subclavian vein and clinical inspection of the ipsilateral arm at every dialysis in 50 consecutive patients who received subclavian hemodialysis catheters over 1 year. Edema of the arm and disappearance of the subclavian vein bruit correctly detected 3 cases of subclavian vein obstruction which were confirmed by X-ray venograms. All 3 cases failed to respond to systemic heparin, but were successfully recanalized within 36 h with continuous local streptokinase infusion at a rate of 10,000 U in 1 ml/h. In 4 other cases of edema of the arm, Doppler examination correctly predicted patency of the vein, also confirmed radiologically. In 2 cases of thrombosis, there was an underlying stenosis of the left innominate vein close to its union with the superior vena cava. These were dilated by balloon angioplasty; the stenosis recurred in both cases without recurrent thrombosis, and the angioplasty was repeated. Doppler surveillance seems to be a promising aid to the detection of subclavian vein thrombosis from hemodialysis catheters. Local streptokinase infusion is effective in treating thrombosis. Underlying venous stenosis should be looked for because this may be at least partly remediable by balloon angioplasty.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".