Comparing the time toxicity of cancer treatments in the CCTG LY.12 trial.
Bibliographic record
Abstract
12135 Background: When different cancer treatments have similar oncologic outcomes, their respective time-related burdens can aid patient-oncologist decision-making regarding which treatment approach to pursue. We have previously developed a pragmatic and patient-centered metric of these time burdens-- which we term ‘’time toxicity’’-- as any day with physical healthcare system contact. This includes outpatient (e.g., bloodwork, scans, clinician, etc.) and emergency room visits, and overnight stays in a healthcare facility. Herein we sought to assess time toxicity in a completed RCT. Methods: We conducted a secondary analysis of the Canadian Cancer Trials Group LY.12 RCT that evaluated 2-3 outpatient 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, transplantation rates, event-free-survival, and overall survival across arms. We calculated patient-level time toxicity by analyzing RCT forms. The study period was the date of assignment to the date of progression or the day before stem cell transplant. We considered days without healthcare contact as ‘’home days’’. We compared time measures across arms. Results: The median study duration was longer in the GDP arm (50, vs. 47 days in the DHAP arms, p=0.007). Median time toxic days were comparable in both arms (18 vs 19 days, p=0.79), but median home days were higher in the GDP arm (33 vs 28 days, p<0.001). The proportion of time toxic days (time toxic days divided by study duration) was lower in the GDP arm (34%, vs. 38%, p=0.009). The GDP arm experienced more time toxicity related to planned outpatient chemotherapy (median, 10 vs. 8 days), but the DHAP arm experienced many more hospitalization days (median, 11 vs. 0 days) (Table). Conclusions: This study demonstrates that measures of time toxicity can be extracted from RCTs. These measures can add clinical insight into patient burdens. In LY.12, despite broadly comparable short- and long-term oncologic outcomes, the GDP arm experienced less time toxicity. Such information can guide decision-making for patients with aggressive hematological cancers, who already spend a significant portion of time with healthcare contact. Clinical trial information: NCT00078949 . [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 | 0.005 | 0.010 |
| 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) | 0.007 | 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".