Bibliographic record
Abstract
Red cell transfusions represent the main treatment of acute blood loss anemia; however, there are patients who are unable or refuse to receive blood products. Further, in all patients, it is essential to only transfuse when the benefits of transfusion outweigh the risks. In those patients who are unable to receive a blood transfusion, it is important to have a multidisciplinary treatment plan. Further, for those patients requiring surgery, it is important to consider the pre-, intra-, and postoperative periods. Many of the principles and strategies outlined in this article can be applied to patients across clinical contexts and are not exclusive to the surgical setting. Before any planned surgery or intervention, all patients should be screened for anemia at least 6 weeks prior to their anticipated surgical or delivery date. Anemia should be corrected, and the patient's bleeding history should be documented. Strategies to reduce blood loss intraoperatively should be discussed with the surgical and anesthetic team. A plan for emergency management of bleeding and treatment to reduce risk, both intraoperatively and postoperatively, should be developed. The patient's decisions about which, if any, blood products or fractions are acceptable should be documented. Intraoperatively, surgical and anesthetic techniques to minimize bleeding should be used along with appropriate medications. Postoperatively, considerations include the minimization of blood sampling, ongoing support of the patient's hemoglobin mass, and optimization of the physiologic tolerance of anemia. Options exist to allow for the safe and effective management of patients who refuse or are unable to receive blood transfusions. It is essential to have clear communication and documentation of the goals of therapy as well as acceptable interventions. The involvement of a multidisciplinary team to manage the patient is essential.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.026 |
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".