Impact of Frailty on Health Care Resource Utilization and Costs of Care in Myelodysplastic Syndromes
Bibliographic record
Abstract
PURPOSE: The role of frailty in affecting survival in myelodysplastic syndromes (MDS) is increasingly recognized. Despite this, a paucity of data exists on the association between frailty and other clinically meaningful outcomes including health care resource utilization and costs of care. METHODS: We linked the Ontario subset of the prospective Canadian MDS registry (including baseline patient/disease characteristics) to population-based health system administrative databases. Baseline frailty was calculated from the 15-item MDS-specific frailty scale (FS-15). Primary outcomes were public health care utilization and 30-day standardized costs of care (2019 Canadian dollars) determined for each phase of disease (initial, continuation, and terminal phases). Negative binomial regression was used to assess the association between frailty and health care costs with Poisson regression to explore predictors of hospitalization. RESULTS: Among 461 patients with complete FS-15 scores, 374 (81.1%) had a hospitalization with a mean length of stay of 10.6 days. Controlling for age, comorbidities, Revised International Prognostic Scoring System, and transfusion dependence, the FS-15 was independently associated with hospitalization during the initial ( P = .02) and continuation ( P = .01) phases but not the terminal disease phase ( P = .09). The mean 30-day standardized cost per patient was $8,499 (median, $6,295; interquartile range, $2,798-$11,996), largely driven by cancer clinic visits and hospitalization. On multivariable analysis, the FS-15 was independently associated with costs of care during the initial disease phase ( P = .02). CONCLUSION: We demonstrate an association between frailty and clinically meaningful outcomes including hospitalization and costs of care in patients with MDS. Our results suggest that baseline frailty may help to inform patients and physicians of expected outcomes.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".