Evaluation of Agreement Among Frailty Assessment Tools in Head and Neck Surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate intertest agreement among hand grip strength (HGS), the modified Frailty Index (mFI), and the Edmonton Frail Scale (EFS) in patients presenting for presurgical assessment in a head and neck surgery clinic. STUDY DESIGN: Prospective observational study. SETTING: Academic tertiary medical center. METHODS: Prospective data relating to 3 frailty measurements were collected for 96 consecutive adults presenting for presurgical counseling at a single high-volume head and neck surgical oncology clinic. Frailty was determined with previously validated thresholds for the mFI (≥3) and EFS (>7). The highest of 2 HGS measurements performed for the dominant hand was used to determine frail status based on previously validated sex- and body mass index-specific thresholds. Baseline characteristics were identified to determine the association of such variables to each tool. Agreement among frailty assessment tools was examined. RESULTS: The frequency of frailty in the cohort varied among tools, ranging from 29.2% (28/96) for HGS to 12.5% (12/96) for the mFI and 4.2% (4/96) for the EFS. The overall agreement among the 3 frailty tools via the Fleiss index was poor (kappa, 0.088; 95% CI, -0.028 to 0.203). CONCLUSION: Assessment of frailty is complex, and established frailty assessment tools may not agree on which patients are frail. When assessing a patient as frail, clinicians must be vigilant to the influence of frailty assessment tools on such determinations, which may contribute critical input during shared decision making for patients considering head and neck surgery or nonsurgical alternatives.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".