Conflating Disability, Frailty, and Multimorbidity in Adult Spinal Deformity Patients
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To examine the degree of overlap between disability, multimorbidity, and frailty in a cohort of ASD patients. SUMMARY OF BACKGROUND DATA: Frailty is a popular topic in spine research, as it is a reported risk factor for poor outcomes. Disability, multimorbidity, and frailty can coexist, sometimes causing or exacerbating one another. It is important to distinguish these conditions for perioperative optimization and to guide research initiatives. METHODS: A multicenter registry of ASD patients was queried for baseline data regarding frailty, as measured by the Edmonton Frail Scale, disability, as measured by the Oswestry Disability Index, and multimorbidity, as measured by the Charlson comorbidity index. The relationships between these measures and both chronological and biological age (PhenoAge) were explored. Exploratory factor analysis (EFA) examined areas of overlap between these diagnoses. RESULTS: There were 861 patients contributing data, mostly female (68%), most undergoing primary surgery at a median age of 66 years [interquartile range (55.1-71.6)], with 6% classified as "Frail." Chronological and PhenoAge showed weak to moderate associations with disability and frailty, though PhenoAge was stronger. There was no evidence of distinct clusters, rather a continuity of condition severity. EFA found overlap between subjective and objective measures of disability, function, and frailty. CONCLUSIONS: Frailty was rare (6%) in this multicenter cohort of patients. Conflation of disability and frailty is a real risk due to overlap in measures of both conditions. Disability and frailty do not form discrete categories but rather exist along a continuum, underscoring the need to abandon categorical labels in favor of continuous measures for both clinical assessment and research settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".