Task Sharing in Global Cardiac Surgery: A Scoping Review
Bibliographic record
Abstract
Background: Access to cardiac surgical services is limited in low- and middle-income countries. The shortage of cardiac surgical care providers is a major contributor to this limited access to lifesaving care. Task shifting and task sharing allow nonspecialists to perform roles typically reserved for specialists such as cardiothoracic surgeons, cardiac anesthesiologists, and other health care workers with specific training in cardiac surgical care. Task sharing has increased access to care without compromising outcomes in related surgical fields, such as general surgery, orthopedic surgery, neurologic surgery, and obstetrics and gynecologic surgery. Methods: A scoping review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews using the databases PubMed/MEDLINE, Embase, World Health Organization Global Index Medicus, and Web of Science. The search was constructed to identify articles specifically addressing task sharing in cardiac surgical care. Results: Four relevant articles were identified. Only 1 focused on task sharing specifically by cardiac surgeons; this was a case study of 2 surgeons at a single hospital who used task sharing with junior surgeons to varying degrees. Two studies discussed task sharing as part of a team-based approach to the management of rheumatic heart disease; 1 study discussed cost-effectiveness in the delivery of cardiac surgical care and included task sharing as 1 approach to cost reduction. Conclusions: There is a paucity of literature describing the applications and outcomes of task sharing in cardiac surgery in variable-resource contexts. Here, we present a scoping review summarizing the literature on experiences with task sharing for cardiac surgical care.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".