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Record W4377993709 · doi:10.1123/jab.2022-0245

Examining the Effect of Time-From-Treatment on Activities of Daily Living Kinematics in Breast Cancer Survivors

2023· article· en· W4377993709 on OpenAlexaff
R. Wills, Jacquelyn M. Maciukiewicz, Marina Mourtzakis, Clark R. Dickerson

Bibliographic record

VenueJournal of Applied Biomechanics · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBreast cancerActivities of daily livingMedicineCancerPhysical therapyPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Breast cancer affects one in 8 females with a 5-year survival rate of 89%. Up to 72% of breast cancer survivors have trouble with activities of daily living (ADL) following treatment. Increased time-from-treatment improves some measures of function, yet ADL limitations persist. Therefore, this study assessed the effect of time-from-treatment on upper extremity kinematics during ADLs in breast cancer survivors. Twenty-nine female breast cancer survivors were divided into 2 groups: <1 year (n = 12) and 1-2 years (n = 17) from treatment. Kinematics were collected during 6 ADL tasks, and humerothoracic joint angles were quantified. A 2-way mixed analysis of variance assessed the effects of time-from-treatment and arm on maximum angles for each ADL. Decreased maximum angle existed for breast cancer survivors with increased time-from-treatment during all ADLs. Breast cancer survivors in the 1-2 years group used ∼28° to 32° lower elevation, ∼14° to 28° lower axial rotation, and ∼10° to 14° lower plane of elevation range across tasks. Decreased ranges of arm movement during ADLs with increased time-from-treatment may reflect compensatory movement strategies. Recognizing this shift in strategies and accompanying underlying disease progression can help inform responses to functional performance limitations in breast cancer survivors as delayed effects are present posttreatment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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