Bibliographic record
Abstract
When the acute dialysis program became an in‐house operation, the development and implementation of a CQI program was a priority. Quality indicators were identified. Clotting in the dialyzer, treatment delays, and catheter‐related infections were tracked. Based on our CQI data, it was clear from the beginning that there was a high incidence of dialyzer clotting, particularly on our patients on Extended Daily Dialysis (EDD) who were on heparin‐free dialysis. Heparin‐free dialysis is prescribed for high bleeding risk patients and for patients with heparin‐induced thrombocytopenia. There was a need to explore an effective way to maintain patency and longevity of the extracorporeal circuit as clotting not only results to blood loss but to loss of treatment time, which affects the efficiency and adequacy of the dialysis therapy. Our policy on no‐heparin dialysis was modified. Hourly saline flushes were changed to a more aggressive every‐15‐to‐30 minute flushes. In addition, “heparin rinse” or priming the extracorporeal circuits with 5000 units of heparin added to 1‐liter bag, except for HIT positive patients, was immediately implemented. After 2 months, clotting in the dialyzer on Extended Daily Dialysis was significantly reduced from 24% to 2%. Conclusion: CQI in the acute dialysis setting is critical for a continuous cycle of evaluating and improving patient outcomes. Through the process of CQI, we were able to identify dialyzer clotting with our EDD as a quality of care problem and implemented a solution that was effective.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".