THE ROLE OF FRAILTY IN PHYSICIANS DECISIONS FOR SEVERE DISABILITY
Bibliographic record
Abstract
Introduction:We aimed to evaluate the correlation between disability approved by the medical board and frailty determined by the Edmonton Frail Scale, which is a tool used to assess frailty. Materials and Method:We enrolled patients admitted to the neurology outpatient clinic of the Bursa Yuksek Ihtisas Training and Research Hospital between 1st-31st March 2019 for examination in order to obtain a disability report from the medical board.Results: Cerebrovascular disease and dementia were more prevalent in older age, while epilepsy, cerebral palsy sequela and other neurological diseases were observed at a younger age.A strong correlation was observed between frailty analysis score and Balthazard disability percentage (p <0,001, r = 0,57).Similarly, there was a correlation between the physicians' severe disability opinion and the Edmonton Frail index score.Scores for cognition, general health status, functional independence, frequency of forgetting to take prescription drugs, or indications of recent weight loss were higher for patients in the severe disability group who also had higher EFS scores.We found that EFS scores >8 correlate significantly with an increased risk of severe disability. Conclusion:We conclude that use of the frailty analysis score in combination with Balthazard disability percentage for patients applying to the medical board could be practical and rational in evaluating the degree of disability and predicting severe disability.Since only patients who applied for medical board evaluation to the neurology clinic were included in our study, our results are relevant for neurology cases and cannot be generalized for all patients who applied for evaluation.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".