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Impact of patient, tumour and treatment factors on psychosocial outcomes in invasive breast cancer.

2021· article· en· W3171382390 on OpenAlexaffabout
David W. Lim, Helene Retrouvey, Isabel Kerrebijn, Kate Butler, Anne C. O’Neill, Tulin Cil, Toni Zhong, Stefan O.P. Hofer, David R. McCready, Kelly Metcalfe

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkMount Sinai HospitalUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsMedicinePsychosocialLumpectomyBreast cancerMastectomyQuality of life (healthcare)Prospective cohort studyStage (stratigraphy)Internal medicineDistressAnxietyCancerGynecologyOncologyClinical psychologyNursingPsychiatry

Abstract

fetched live from OpenAlex

568 Background: In breast cancer, clinicians aim to improve survival while patients value quality of life. We aim to delineate the impact of patient, tumour and treatment factors on psychosocial outcomes after treatment. Methods: A prospective cohort of women with unilateral stage I-III breast cancer were recruited at University Health Network in Toronto, Canada between 2014-2017. Validated questionnaires (BREAST-Q, Impact of Event, Hospital Anxiety & Depression Scales) were completed pre-operatively, and 6 and 12 months after surgery. Change in psychosocial scores over time by surgical procedure was assessed using linear mixed models, controlling for age, pathologic stage, hormone (HR) and HER2 receptor, and treatments. Predictors of psychosocial outcomes at 12 months were assessed using multivariable linear regression models. P values <.05 were significant. Results: 413 women underwent unilateral lumpectomy (48%), unilateral mastectomy (36%) and bilateral mastectomy (16%). Pathologic stage were: 18 ypT0/Tis (4%), 201 stage I (49%), 136 stage II (33%) and 58 stage III (14%). Receptor profiles were as follows: 277 HR+/HER2- (68%), 59 HR+/HER2+ (14%), 31 HR-/HER2+ (8%) and 39 HR-/HER2- (10%). Over time, women having unilateral lumpectomy had the highest scores of breast satisfaction ( P<.01), psychosocial ( P<.01) and sexual ( P<.01) well-being, with no difference between unilateral versus bilateral mastectomy groups. Age was inversely related with distress ( P <.01), psychosocial ( P <.01) and physical ( P =.001) well-being. Radiotherapy was associated with worse breast satisfaction (-8.1, P<.01), psychosocial (-6.9, P<.01) and physical (-5.8, P<.01) well-being, while chemotherapy was associated with worse sexual well-being (-5.5, P=.04). Endocrine therapy was associated with worse distress (6.7, P <.01), physical (-5.2, P <.01) and sexual (-6.4, P =.03) well-being. Women with a pathologic complete response had less anxiety compared to stage I (-2.0, P=.03). Women with triple-negative disease had worse breast satisfaction (-8.0, P=.03), distress (8.0, P =.01), anxiety (2.4, P <.01) and psychosocial (-7.5, P =.047) well-being than HR+/HER2- disease. In our regression model at 12 months, surgical procedure was a significant predictor of breast satisfaction ( P <.01), psychosocial ( P<.01), physical ( P<.01) and sexual ( P<.01) well-being. HER2 positivity predicted worse satisfaction ( P=.045), psychosocial ( P =.047), physical ( P =.02) and sexual ( P =.01) well-being. Income level ( P=.01) predicted breast satisfaction and physical well-being. Ethnicity (P <.01) and education level (P =.04) predicted distress scores. Conclusions: Psychosocial functioning after breast cancer is influenced by an interplay between patient, tumour and treatment factors. Delineating these influences identifies potentially modifiable factors with de-escalation therapy and enhancing psychosocial support.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.489
Teacher spread0.385 · 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 designObservational
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".

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Citations2
Published2021
Admission routes2
Has abstractyes

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