Bibliographic record
Abstract
Introduction: Vascular access failure is one of the greatest sources of morbidity for chronic hemodialysis patients. Prophylactic and repeated measurement of access flow may be of importance in preventing clotting. The aim of the study was therefore to analyse the cost effectiveness of a shunt surveillance program, which reduces the appearance of occlusion of the vascular access. Methods: The number of vascular access interventions (surgery and radiology) in the period 2001 till 2003 (transonic measurement period, TMP; 63 patients) was compared with a reference period (RP, 1996 till 1998) during which no access flow was measured (58 patients). All measurements were done with Transonic® and interventions according to K/DOQI. Results: During the RP, 123 vascular access operations (0.71 per patient year) were performed because of occlusion, whereas in the TMP 58 vascular access operations (0.3 per patient year) were performed. During the TMP, 298 angiographic measurements were performed (1.6 per patient year) in the RP 177, (1.0 per patient year). In the TMP, 1652 access flow measurements were performed. In order to prevent one shunt occlusion, 21 access flow measurements had to be performed. Total costs in the TMP (summary of angiography, angiography and PTA, hospitalization days, and operation costs) are reduced with 31% compared to the RP; costs per patient year in RP: €2315. Costs per patient year in TMP: €1606. Conclusion: By means of a shunt surveillance program (based on access flow measurement), if necessary followed by angiography, it is possible to reduce the number of acute vascular access occlusions. Although a shunt surveillance program may take up a lot of time for the nursing staff, the beneficial effects, lower costs, and reduced morbidity for the patients outweigh this effort.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".