Bibliographic record
Abstract
PURPOSE: All physicians who use heparin should be aware of immune heparin-induced thrombocytopenia (HIT), including anesthesiologists who may need to provide intraoperative anticoagulation for a patient who urgently requires cardiac or vascular surgery but who has acute HIT or a history of recent HIT. SOURCE: The literature dealing with HIT of relevance to anesthesiologists was reviewed, including studies of HIT antibody formation following intraoperative use of heparin; acute respiratory or cardiac arrest following i.v. bolus heparin indicating rapid-onset HIT; acute thrombocytopenia and thrombosis complicating intraoperative heparin use; circumstances in which it might be acceptable to administer heparin despite a previous history of immune HIT; and alternative anticoagulant approaches that can be used to manage cardiac or vascular surgery in a patient with acute or recent HIT. PRINCIPAL FINDINGS: Intraoperative exposure to heparin can trigger formation of HIT antibodies, and occasionally even lead to "delayed-onset" HIT. Acute respiratory or cardiac arrest following i.v. bolus heparin, or the abrupt occurrence of intraoperative "white clots," suggests a diagnosis of rapid-onset HIT, particularly if the patient recently received heparin. Several approaches are available to manage cardiac or vascular surgery in a patient with acute or recent HIT, so the treatment chosen depends upon local experience and monitoring capabilities. Several months after acute HIT, and particularly when HIT antibodies are no longer detectable, it may be acceptable to use heparin for intraoperative anticoagulation. CONCLUSION: HIT is an infrequent but important topic for anesthesiologists because of the urgency and complexity of the various associated management issues.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".