MAIN COMPLICATIONS IN THE POST-OPERATORY OF HEART SURGERIES: SCOPING REVIEW
Bibliographic record
Abstract
It is a scoping review, developed based on the guidelines proposed by the Guidance for the Conduct of Scoping Reviews, of the Joanna Briggs Institute (JBI), in its 2017 manual (PETERS et al., 2017). OBJECTIVE: To identify and map the main complications in the postoperative period of cardiac surgery. FORMULATION OF THE RESEARCH QUESTION: P (Population) - Adult patients undergoing cardiac surgery. C (Concept) - Postoperative complications. C (Context) - Postoperative of cardiac surgery. What are the main complications that occur in adult patients undergoing cardiac surgery? ELIGIBILITY CRITERIA: Texts available in full, in Portuguese, Spanish and English; Texts that address complications in the postoperative period of cardiac surgery in adults. MESH TERMS: P: Patient; Patients. C: Postoperative complications; postoperative complication; surgical complication. C: Thoracic surgery; cardiac surgery; heart surgery; cardiac surgical procedures; heart surgical procedures; Myocardial Revascularization; Surgery Heart Diseases; Previous Cardiac Surgery; Cardiovascular Surgical Procedures; cardiac bypass surgery; video-assisted thoracic surgery. DATA BASES: PubMed, CINAHL, Web of Science, Scopus, LILACS, CAPES Theses and Dissertations Portal, DART-Europe E-Theses Portal, Electronic Theses Online Service (EThOS), Scientific Open Access Repository of Portugal (RCAAP), Trove, National ETD Portal, Theses Canada.
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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 | 0.029 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.031 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".