Plasma: indications, controversies, and opportunities
Bibliographic record
Abstract
Plasma is overused as a blood product worldwide; however, data supporting appropriate use of plasma is scant. Its most common utilization is for treatment of coagulopathy in actively bleeding patients; it is also used for coagulation optimization prior to procedures with specific coagulation profile targets. A baseline literature review in PUBMED and Google Scholar was done (1 January 2000 to 1 June 2023), utilizing the following search terms: plasma, fresh frozen plasma, lyophilized plasma, indications, massive transfusion protocol, liver disease, warfarin reversal, cardiothoracic surgery, INR < 2. An initial review of the titles and abstracts excluded all articles that were not focused on transfusional medicine. Additional references were obtained from citations within the retrieved articles. This narrative review discusses the main indications for appropriate plasma use, mainly coagulation factor replacement, major hemorrhage protocol, coagulopathy in liver disease, bleeding in the setting of vitamin K antagonists, among others. The correlation between concentration of coagulation factors and INR, as well as the proper plasma dosing with its volume being weight-based, is also discussed. A high value approach to plasma utilization is supported with a review of the clinical situations where plasma is overutilized or unnecessary. Finally, a discussion of novel plasma products is presented for enhanced awareness.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".