The Hodgkin lymphoma international study for individual care (HoLISTIC): Enhancing decision making in pediatric and adult Hodgkin lymphoma (HL).
Bibliographic record
Abstract
e20019 Background: Decision making in HL is complicated by clinical trial results that differ, a growing range of treatment options, and the absence of ideal, objective information on long term outcomes from modern therapy. Methods: We formed an international consortium, HoLISTIC, consisting of 50+ pediatric & adult HL providers, decision scientists, statisticians, epidemiologists, and patient (pt) advocates and are creating a data repository of individual pt data (IPD) from 16 contemporary, pediatric & adult clinical trials for newly diagnosed pts with HL, and 6 large HL registries/survivorship cohorts, the latter enriched with LE data (Table). We will enhance our prior decision model (DM) from group-level data (Parsons S et al. B J Haem 2018) to establish a dynamic HL DM from IPD. Using statistical and simulation modeling of IPD, the enhanced DM will project outcomes of interest, including quality-adjusted life years (QALYs), reflecting both mortality and morbidity. Results will be validated and calibrated against prominent external cohorts (e.g., St. Jude LIFE Cohort, Dutch HL registry). The DM then will be converted to a web-based platform that we will test and evaluate among HL providers and pts at the point of care. Results: To date, we have harmonized IPD from 10 trials (~8,000 HL pts), ranging in size from 165-1925 pts. At diagnosis, median age was 26 y (IQR 18-38); 51.5% were male. 43% had B symptoms, 34% had mediastinal bulk, and 79% had nodular sclerosis histology. Median follow up was 5.0 y (IQR 3.5-7.4). IPD harmonization is ongoing, which will be followed by creation of the enhanced DM. Conclusions: HoLISTIC capitalizes on a multidisciplinary pediatric & adult oncology collaborative, harmonizing extensive IPD by linking data from clinical trials and real world registries/survivorship cohorts. This work will inform questions about the influence of Tx options on both acute and potential long term events and how those options align with pt values and preferences. [Table: see text]
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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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".