Making mobility-related disability better: a complex response to a complex problem
Bibliographic record
Abstract
Mobility disability in older adults can arise from single system problems, such as discrete musculoskeletal injury. In frail older adults, however, mobility disability is part of a complex web of problems. The approach to their rehabilitation must take that complexity into account, as is reported by Fairhall et al. First, their overall health state must be assessed, which is achieved by a comprehensive geriatric assessment. The assessment can show how a particular patient came to be disabled, so that an individualized care plan can be worked out. Whether this approach works in general can be evaluated by looking at group differences in mean mobility test scores. Knowing whether it has worked in the individual patient requires an individualized measure. This is because not every patient starts from the same point, and not every patient achieves success by aiming for the same goal. For one patient, walking unassisted for three metres would be a triumph; for another it would be a tragedy. Unless we understand the complexity of the needs of frail older adults, we will neither be able to treat them effectively nor evaluate our efforts sensibly.Please see related article http://www.biomedcentral.com/1741-7015/10/120.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".