MétaCan
Menu
← Back to cohort

Effects of Non-Linear or Linear Aerobic Training (AT) Dosing Regimens on Impaired Cardiovascular (CV) Function in Patients with Operable Breast Cancer: A Randomized Controlled Trial (RCT).

2019· article· en· W2947496453 on OpenAlexaff
Jessica M. Scott, Samantha M. Thomas, James E. Herndon, Jeffrey Peppercorn, Pamela S. Douglas, Michel G. Khouri, Chau T. Dang, Anthony F. Yu, Diane Catalina, Cristi Ciolino, Catherine Capaci, Meghan Michalski, Neil D. Eves, Lee W. Jones

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMedicineBreast cancerRandomized controlled trialTolerabilityAdverse effectInternal medicineClinical endpointDosingVO2 maxCancerSurgeryHeart rateBlood pressure

Abstract

fetched live from OpenAlex

541 Background: Breast cancer therapy causes marked impairments in CV function predisposing to elevated risk of CV morbidity. We investigated the effects of two AT dosing regimens on CV function in post-treatment patients with operable breast cancer. Methods: In a three-arm, parallel-group RCT, 174 post-menopausal patients (2.8 years post primary adjuvant therapy) with impaired age/sex-matched peak oxygen consumption (VO2peak) were randomized to: (1) conventional linear AT (uniform dose-intensity / session), (2) nonlinear AT (variable dose-intensity / session), or (3) stretching (attention control). AT consisted of 64 supervised treadmill walking sessions delivered four times weekly at either ~70% VO2peak for 40 mins/session (linear) or 55% to 100% VO2peak for 20-45 mins/session (nonlinear) for 16 consecutive weeks. Stretching was matched to AT on the basis of location, frequency, duration, and treatment length. The primary end point was change in VO2peak. Secondary end points were other markers of CV risk profile (biochemical CV risk profile, cardiac function, body composition), patient-reported outcomes (PROs), tolerability (e.g., relative dose intensity), and safety. All analyses followed the intention-to-treat principle. Results: Rates of lost-to-follow were < 10% in all arms. Relative dose intensity of AT was 73% ± 27% and 80% ± 21% in linear and nonlinear arms, respectively. No serious adverse events were observed. In adjusted analysis, compared to control, VO2peak (ml O2.kg-1.min-1) increased 0.7 (± 0.3) ml O2.kg-1.min-1 (p = 0.06) and 0.8 (± 0.4) ml O2.kg-1.min-1 (p = 0.02) in linear and nonlinear AT, respectively. Rates of VO2peak improvement greater than the technical error of measurement (i.e., ≥1.32ml O2.kg-1.min-1) were 33% and 39% in linear and nonlinear AT (p = 0.03), respectively. Both AT regimens were associated with improvements in several secondary CV end points but only nonlinear AT improved PROs compared with control (all p’s < 0.05). There were no differences between the two AT regimens. Conclusion: AT significantly improves CV function and PROs in post-treatment breast cancer patients. The efficacy-tolerability ratio favors the non-linear regimen over the conventional linear prescription approach. Clinical trial information: NCT01186367.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
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 head0.032
GPT teacher head0.353
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicCancer survivorship and care→French-language works237,207→