Breaking the Age Barrier: Physicians’ Perceptions of Candidacy for Allogeneic Hematopoietic Cell Transplantation in Older Adults
Bibliographic record
Abstract
Despite continuing increases in the use of allogeneic hematopoietic cell transplantation (alloHCT) in older adults, no standardized geriatric assessment (GA) has been established to risk stratify for transplantation-related morbidity. We conducted a survey of transplant physicians to determine perceptions of the impact of older age (≥60 years) on alloHCT candidacy, and utilization of tools to gauge candidacy. This 23-item online cross-sectional survey was distributed to HCT physicians caring for adults in the United States between May and July 2019. Of the 770 invited HCT physicians, 175 (22.7%) completed the survey. The majority of respondents were age 41 to 60 years and male and practiced in a higher-volume teaching hospital. When considering regimen intensity, 29 physicians (17%) stated they would consider a myeloablative regimen for patients age ≥70 years, and 141 (82%) would consider reduced-intensity/nonmyeloablative conditioning for patients age ≥70 years. Almost all (90%) endorsed the need for a specialized assessment of pre-HCT vulnerabilities to guide candidacy decisions for older adults. Most physicians reported that their centers rarely (33%) or never (46%) use a dedicated geriatrician/geriatric-oncologist to assess alloHCT candidates age ≥60 years. Common barriers to performing a GA included uncertainty about which tools to use, lack of knowledge and training, and lack of appropriate clinical support staff. Many alloHCT physicians will consider alloHCT in patients up to age 75 years and not uncommonly in patients older than that. However, the application of tools and domains to assess candidacy in older adults varies widely. Incorporation of a standardized pretransplantation health assessment tool for risk stratification is a significant unmet need.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".