Real‐world evidence of heparin and citrate use in extracorporeal photopheresis: A hypothesis‐generating data review of device settings and performance
Bibliographic record
Abstract
Extracorporeal photopheresis (ECP) is widely used for the treatment of cutaneous T-cell lymphoma, graft-vs-host disease, and other immune-related conditions. To avoid clotting during treatment, the ECP system used must be effectively primed with an anticoagulant. Heparin is the recommended anticoagulant for the THERAKOS CELLEX System, but acid citrate dextrose-A (ACDA) is often used. We compared system performance between these two anticoagulants for this ECP system. Deidentified data for ECP device performance were obtained at each treatment session, from automatically logged Smart Cards or labels completed by device operators. We compared the effects of ACDA or heparin on overall treatment duration, buffy coat (leukocyte) collection time, photoactivation time and the number of alarms and warnings. The variability in these parameters was also assessed. Data from 23 334 treat sessions were analyzed; ACDA was used in 34.4% and heparin in 65.6%. Overall, the ECP procedure duration, buffy coat collection time and photoactivation time were numerically similar regardless of whether ACDA or heparin was used, and regardless of needle mode. Photoactivation time variability was lower with ACDA compared with heparin in all needle modes. Among treatments that were completed automatically without any operator intervention, total treatment duration and photoactivation time were significantly reduced with ACDA use in both the double- and single-needle modes. The data presented indicate that, in both double- and single-needle modes, the THERAKOS® CELLEX® integrated ECP system performed similarly with ACDA compared to heparin, although ACDA demonstrated potential benefits in reducing variability in photoactivation time.
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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 | 0.051 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".