Geriatric Assessment Reveals Actionable Impairments in Hematopoietic Stem Cell Transplantation Candidates Age 18 to 80 Years
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (HCT) is a potentially curative treatment for both malignant and nonmalignant hematologic diseases; however, reported rates of treatment-related mortality approach 30%. Outcomes are worse in patients who begin HCT with functional impairments. To detect such impairments, a geriatric assessment (GA) is recommended in adults age ≥65 years. Younger HCT candidates also may be impaired because of chemotherapy regimens pre-HCT. Therefore, we hypothesized that GA can be beneficial for adult patients of all ages and subsequently created a clinical pretransplantation optimization program to assess all HCT candidates using a modified GA. One-hundred fifty-seven patients were evaluated in 4 functional domains- physical, cognitive, nutritional, and psychological-at 2 time points prior to HCT-new patient evaluation (NPE) and sign-off (SO)-between October 2017 and January 2020. At NPE, 80.9% of the patients had at least 1 domain with a functional impairment, and physical (P = .006), cognitive (P = .04), and psychological (P = .04) impairments were associated with an increased likelihood of not proceeding to HCT. In addition, patients age 18 to 39 years were more likely than older patients to have a physical function impairment (P = .001). Between NPE and SO, 51.9% of the patients had resolution of 1 or more impairments, and nutritional impairment at SO was predictive of worse overall survival (P = .01). Our study shows that GA can identify functional impairments in patients of all ages. Early identification of impairments could facilitate referrals to supportive care and resolution of impairments prior to HCT, suggesting that GA could be recommended for HCT candidates of all ages.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".