Contact Days Associated With Cancer Treatments in the CCTG LY.12 Trial
Bibliographic record
Abstract
BACKGROUND: When cancer treatments have similar oncologic outcomes, the number of days with in-person healthcare contact (""contact days'') can help contextualize expected time use with each treatment. We assessed contact days in a completed randomized clinical trial. PATIENTS AND METHODS: We conducted a secondary analysis of the CCTG LY.12 RCT that evaluated 2-3 cycles of gemcitabine, dexamethasone, and cisplatin (GDP) vs. dexamethasone, cytarabine, and cisplatin (DHAP) in 619 patients with relapsed/refractory lymphoma prior to stem cell transplant. Primary analyses reported similar response rates and survival. We calculated patient-level "contact days" by analyzing trial forms. The study period was from assignment to progression or transplant. Days without healthcare contact were considered "home days''. We compared measures of contact days across arms. RESULTS: The study period was longer in the GDP arm (median 50, vs. 47 days, P = .007). Contact days were comparable in both arms (median 18 vs 19, P = 0.79), but home days were higher in the GDP arm (median 33 vs 28, P < .001). The proportion of contact days was lower in the GDP arm (34%, vs. 38%, P = .009). The GDP arm experienced more contact days related to planned outpatient chemotherapy (median, 10 vs. 8 days), but the DHAP arm experienced many more inpatient contact days (median, 11 vs. 0 days). CONCLUSIONS: Measures of time use, such as contact days, can be extracted from RCTs. In LY.12, despite comparable oncologic outcomes, GDP was associated with fewer contact days. Such information can guide decision-making for patients with hematological cancers, who already face significant healthcare contact.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".