Surgical frailty assessment: a missed opportunity
Bibliographic record
Abstract
Abstract Background Preoperative frailty predicts adverse postoperative outcomes. Despite the advantages of incorporating frailty assessment into surgical settings, there is limited research on surgical healthcare professionals’ use of frailty assessment for perioperative care. Methods Healthcare professionals caring for patients enrolled at a Canadian teaching hospital were surveyed to assess their perceptions of frailty, as well as attitudes towards and practices for frail patients. The survey contained open-ended and 5-point Likert scale questions. Responses were compared across professions using independent sample t-tests and correlations between survey items were analyzed. Results Nurses and allied health professionals were more likely than surgeons to think frailty should play a role in planning a patient’s care (nurses vs. surgeons p = 0.008, allied health vs. surgeons p = 0.014). Very few respondents (17.5%) reported that they ‘always used’ a frailty assessment tool. Results from qualitative data analysis identified four main barriers to frailty assessment: institutional, healthcare system, professional knowledge, and patient/family barriers. Conclusion Across all disciplines, the lack of knowledge about frailty issues was a prominent barrier to the use of frailty assessments in practice, despite clinicians’ understanding that frailty affects their patients’ outcomes. Confidence in frailty assessment tool use through education and addressing barriers to implementation may increase use and improve patient care. Healthcare professionals agree that frailty assessments should play a role in perioperative care. However, few perform them in practice. Lack of knowledge about frailty is a key barrier in the use of frailty assessments and the majority of respondents agreed that they would benefit from further training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 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; both teacher heads agree on what is shown here.
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".