Physician frontline treatment preferences for stage III/IV classic Hodgkin lymphoma: the real-world US CONNECT study
Bibliographic record
Abstract
Aim: To understand US physicians' frontline (1L) treatment preferences/decision-making for stage III/IV classic Hodgkin lymphoma (cHL). Materials & methods: Medical oncologists and/or hematologists (≥2 years' practice experience) who treat adults with stage III/IV cHL were surveyed online (October–November 2020). Results: Participants (n = 301) most commonly considered trial efficacy/safety data and national guidelines when selecting 1L cHL treatments. Most physicians (91%) rated overall survival (OS) as the most essential attribute when selecting 1L treatment. Variability was seen among regimen selection for hypothetical newly diagnosed patients, with OS cited as the most common reason for regimen selection. Conclusion: While treatment selection varied based on patient characteristics, US physicians consistently cited OS as the top factor considered when selecting a 1L treatment for cHL.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".