Bibliographic record
Abstract
OBJECTIVE: To gain contemporary insights from residents and surgeons regarding the care of older surgical patients. BACKGROUND DATA: With worldwide aging, efforts over the past decade have attempted to increase surgeons' abilities to care for older adults, but a current understanding of attitudes, knowledge, practices, and needs is missing. METHODS: Between July 2016 and September 2016 we conducted a national Web-based survey sampling all general surgery residents and academic general surgeons using a questionnaire designed and tested for this purpose. Summative scales within each domain (attitudes, knowledge, practices, and needs) were created and compared between groups. Open-ended responses were analyzed with thematic analysis. RESULTS: Ninety-four of 172 invited residents (55%) and 80 of 243 invited surgeons (33%) across 14 general surgery programs responded with no missing data. Both groups had favorable attitudes (83% vs 68%, P = 0.02). However, 80% of residents and 76% of surgeons had medium-level knowledge test scores, and few had prior training. Most respondents reported only sometimes performing guideline-recommended practices (71% vs 73%, P = 0.55). Gaps in training and care delivery were identified. Residents wanted focused, high-yield materials and case-oriented practical skills training. Respondents reported further improvements may come from building surgeons' capacity, enhancing collaboration including perioperative geriatric services, better preoperative assessment, increased adherence to perioperative guidelines, and greater community-based supports to recovery. CONCLUSIONS: Residents and surgeons have favorable attitudes, but only moderate geriatric-specific knowledge and only some guideline-adherent practices. We identified gaps in training and care delivery with targets for future knowledge translation and quality improvement initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".