Bibliographic record
Abstract
Unfractionated heparin remains the most commonly used anticoagulant for extracorporeal therapies worldwide due to cost, years of clinical experience showing effectiveness and safety for outpatient hemodialysis. Most centers administer unfractionated heparin as an initial bolus followed by a constant infusion, which is then stopped prior to the end of the dialysis session. Although the anticoagulant effect of heparin can be monitored at the bedside, most centers take a pragmatic stance for routine outpatient hemodialysis, and adjust bolus doses and infusion rates based on visual inspection of the dialyzer header and venous air detector chamber for clots, and stop the heparin infusion based on the time taken for needle puncture sites to stop bleeding at the end of the hemodialysis session. Heparin is negatively charged and can bind to plasma proteins, leukocytes, and plastic. As such, it is important to achieve adequate mixing of heparin with blood to optimize anticoagulation within the extracorporeal circuit, by administering an intravenous bolus a few minutes prior to connecting the patient to the circuit and ensuring thorough mixing of the heparin infusion.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".