Bibliographic record
Abstract
Aim: Modified-Krishnan’s frailty index (FI) is an FI calculation method developed by Krishnan et al. in 2014. This study aimed to compare the effectiveness and correlation of the FIs from Krishnan and the Canadian study of health and aging (CSHA) in predicting postoperative outcomes of elderly patients with hip fracture. Methods: Based on clinical follow-up and observation, we utilized these two instruments to predict 3-month mortality, hip function, and recovery of daily activities. The area under the curve (AUC) and the Pearson correlation coefficient were used to compare the two scales’ predictive validities for postoperative outcomes. Results: A total of 130 patients were included; 67% female and mean age 77.5 ± 8.5 years. The AUCs of modified-Krishnan’s FI (AUC = 0.856; 95% confidence interval (CI) = 0.767–0.945) and the CSHA-FI (AUC = 0.793; 95% CI = 0.652–0.934) were used to compare the effectiveness in predicting patient mortality. The optimal predictive scores were 0.335 and 0.28, respectively. The Pearson correlation analysis showed that the modified-Krishnan’s FI correlated with the Japanese Orthopaedic Association hip score (pain, activity, walking ability, and ability for daily living; R = −0.249, p = 0.005), while the CSHA-FI was not correlated ( R = −0.125, p = 0.170). The modified-Krishnan’s FI ( R = −0.415, p < 0.001) and the CSHA-FI ( R = −0.332, p < 0.001) were both significantly correlated with the functional recovery scale score. Conclusions: The modified-Krishnan’s FI and the CSHA-FI were effective in the prediction of postoperative mortality. But the modified-Krishnan’s FI was more consistently associated with the recovery of hip function and daily activities at 3 months after the operation than that of the CSHA-FI. The modified-Krishnan’s FI was more suitable to utilize for risk stratification, identifying deficits, and predicting recovery capacity in hip fracture patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".