Deciphering the Interplay of Frailty, Age, and Gender in Orthopedic Surgical Outcomes Among the Elderly: Insights From a Prospective Cohort Study
Bibliographic record
Abstract
Background: With India’s aging population on the rise, the prevalence of frailty among elderly patients undergoing major orthopedic surgeries presents a significant challenge for healthcare systems. Frailty, characterized by decreased physiological reserve and increased vulnerability to adverse health outcomes, necessitates a comprehensive approach to preoperative evaluation and care. This study aims to explore the correlation between frailty and socio-demographic variables, particularly age and gender, utilizing the Edmonton Frailty Scale (EFS) to assess frailty among elderly orthopedic surgery patients. Material and Methods: A prospective cohort study was conducted, encompassing 157 patients aged 60 years and above, undergoing major orthopedic procedures between June 2019 and June 2021. The EFS was employed to evaluate frailty, categorizing patients across a spectrum from ‘Not Frail’ to ‘Severe Frail’. Statistical analysis was performed to examine the relationship between frailty levels and socio-demographic variables. Results: The majority of participants were males (59.2%) in the age group of 60-65 years (63.7%). The distribution of frailty revealed 40.1% of patients as not frail, with a substantial proportion displaying varying degrees of frailty. A significant correlation was found between increased frailty severity and advancing age ( P < .001), while gender differences in frailty distribution suggested a higher predisposition towards severe frailty among females. Conclusion: The study underscores the high prevalence of frailty among elderly orthopedic patients and its significant association with age and gender. These findings highlight the necessity for frailty-informed preoperative assessments and interventions tailored to the specific needs of elderly patients. Incorporating frailty evaluations into clinical practice can enhance surgical outcomes and improve the quality of care for this vulnerable population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".