Difference among frailty assessment tools in predicating postoperative prognosis of elderly patients with mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: Mild Traumatic Brain Injury (mTBI) is quite prevalent in the elderly population, and the authors performed a retrospective analysis regarding the predictive value of frailty assessing tools regarding the prognosis of elderly mTBI patients. METHODS: All the patients underwent assessment of frailty upon admission using five tools including Frailty Phenotype (FP), FRAIL Scale (FS), Edmonton Frailty Scale (EFS), Groningen Frailty Indicator (GFI), and Clinical Frailty Scale (CFS). The predicting potential of tools was analyzed against the prognosis defined by the extended Glasgow Outcome Scale (GOSE). RESULTS: The incidence of frailty in elderly patients varies widely among the tools. Multivariate logistic regression analysis showed that only frail conditions defined by FP (p-value = 0.014) and FS (p-value = 0.004) could be employed for predicting unfavorable prognosis defined by GOSE, while frailty defined by CFS (p-value = 0.683), EFS (p-value = 0.301) and GFI (p-value = 0.925) could not. The ROC further showed that FP (AUC = 73.2 %) and FS (AUC = 76.2 %) had moderate power in predicting unfavorable conditions, while CFS (AUC = 46.1 %), EFS (AUC = 55.6 %), and GFI (AUC = 51.5 %) only had low or even no power. CONCLUSIONS: FP and FS could be used to predict the unfavorable prognosis associated with mTBI in the elderly 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 | 0.000 | 0.001 |
| 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".