An expanding role for cardiopulmonary bypass in trauma.
Bibliographic record
Abstract
OBJECTIVES: To analyze experience at the McGill University Health Centre with cardiopulmonary bypass (CPB) in trauma, complemented by a review of the literature to define its role globally and outline indications for its expanded use in trauma management. DATA SOURCES: All available published English-language articles from peer reviewed journals, located using the MEDLINE database. Chapters from relevant, current textbooks were also utilized. STUDY SELECTION: Nine relevant case reports, original articles or reviews pertaining to the use of CPB in trauma. DATA EXTRACTION: Original data as well as authors' opinions pertinent to the application of CPB to trauma were extracted, incorporated and appropriately referenced in our review. DATA SYNTHESIS: Overall mortality in the selected series of CPB used in the trauma setting was 44.4%. Four of 5 survivors had CPB instituted early (first procedure in operative management) whereas 3 of 4 deaths involved late institution of CPB. CONCLUSIONS: Although CPB has traditionally been used in the setting of cardiac trauma alone, a better understanding of its potential benefit in noncardiac injuries will likely make for improved outcomes in the increasingly diverse number of severely injured patients seen in trauma centres today. Further studies by other trauma centres will allow for standardized indications for the use of CPB in trauma.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".