Characteristics of Health Care Settings Where Adolescents and Young Adults Receive Care for ALL
Bibliographic record
Abstract
PURPOSE: Individuals diagnosed with cancer between 15 and 39 years (adolescent and young adult [AYA]) face unique vulnerability. Detail is lacking about care delivery for these patients, especially those with ALL. We address these knowledge gaps by describing AYA ALL care delivery details at National Cancer Institute Community Oncology Research Program (NCORP) (sub)affiliates by model of care. METHODS: Participating institutions treated at least one AYA with ALL from 2012 to 2016. Study-specific criteria were used to determine the number of unique clinical facilities (CFs) per NCORP and their model of care (adult/internal medicine [IM], pediatric, mixed [both]). Surveys completed by NCORPs for each CF by model of care captured size, resources, services, and communication. RESULTS: Among 84 participating CFs (adult/IM, n=47; pediatric, n=15; mixed, n=24), 34% treated 5-10 AYAs with ALL annually; adult/IM CFs more often treated <5 (adult/IM, 60%; pediatric, 40%; mixed, 29%). Referral decisions were commonly driven by an age/diagnosis combination (58%), with frequent ALL-specific age minimums (87%) or maximums (80%). Medical, navigational, and social work services were similar across models while psychology was available at more pediatric CFs (pediatric, 80%; adult/IM, 40%; mixed, 46%-54%). More pediatric or mixed CFs reported oncologists interacting with pediatric/adult counterparts via tumor boards (pediatric, 93%; adult/IM, 26%; mixed, 96%) or initiating contact (pediatric, 100%; adult/IM, 77%; mixed 96%); more pediatric CFs reported an affiliated counterpart (pediatric, 53%; adult, 19%). Most CFs reported no AYA-specific resources (79%) or meetings (83%-98%). CONCLUSION: System-level aspects of AYA ALL care delivery have not been examined previously. At NCORPs, these characteristics differ by models of care. Additional work is ongoing to investigate the impact of these facility-level factors on guideline-concordant care in this population. Together, these findings can inform a system-level intervention for diverse practice settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".