A Scoping Review on Goals of Care Discussions in Surgery: How Are We Doing and How Can We Do Better?
Bibliographic record
Abstract
BACKGROUND: Discussing GOC is essential to ensuring that patients' treatment recommendations and care plans are aligned with their preferences, priorities, and values. This review aims to characterize the existing literature on the quality, practices, and frameworks of goals of care (GOC) discussions in surgery to identify gaps and propose strategies for improvement. METHODS: MEDLINE, MEDLINE In-Process/ePubs, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science, Scopus, and ClinicalTrials.Gov were searched using terms related to GOC, surgery, and best practices or education. The search strategy was run from inception to July 29, 2022. Studies regarding the quality of GOC discussions in surgery were included. RESULTS: The search identified 14,254 articles from which 37 were included for review. Key findings included (1) the reactive nature of GOC discussions and initiating conversations in response to acute health changes, (2) ambiguity around patient autonomy and the surgeon's duty to prioritize surgical treatment, (3) surgeons as curators of information, and (4) tendency of surgeons to provide a set of standard treatment pathways and determine specific care decisions rather than establish understanding of patients' long-term goals. CONCLUSION: Further research is needed to determine best practices for caregiver and next-of-kin involvement and expand the diversity of reported experiences to include patients from diverse ethnic backgrounds and genders and individuals from rural and lower-resource communities. Findings from this review have important implications for improving GOC conversations to ensure they support patient-centered care.
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 | 0.031 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".