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Comparing the time toxicity of cancer treatments in the CCTG LY.12 trial.

2023· article· en· W4379340492 on OpenAlexafffundabout
Arjun Gupta, Annette E. Hay, Michael Crump, Marina Djurfeldt, Liting Zhu, Matthew C. Cheung, Lois E. Shepherd, Bingshu E. Chen, Christopher M. Booth

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsSunnybrook Health Science CentrePrincess Margaret Cancer CentreHealth Sciences CentreUniversity Health NetworkQueen's University
FundersCanadian Cancer Society Research Institute
KeywordsMedicineGemcitabineToxicityInternal medicineRandomized controlled trialCancerOncologySurgery

Abstract

fetched live from OpenAlex

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]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.315
GPT teacher head0.454
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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