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Record W2161799794 · doi:10.1200/jco.2007.11.9529

Aromatase Inhibitors and Arthralgias: A New Frontier in Symptom Management for Breast Cancer Survivors

2007· letter· en· W2161799794 on OpenAlexaboutno aff
Harold J. Burstein, Eric P. Winer

Bibliographic record

VenueJournal of Clinical Oncology · 2007
Typeletter
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerAromataseInternal medicineTamoxifenCancerAdjuvant therapyOncologyOsteoporosisAromatase inhibitorGynecology

Abstract

fetched live from OpenAlex

Aromatase inhibitors (AIs) have become widely used as adjuvant therapy for postmenopausal women with hormone receptor– positive breast cancer. Although the optimal schedule and duration remain unclear, current guidelines endorse the inclusion of AI treatment at some point during adjuvant therapy for most postmenopausal women with estrogen receptor–positive breast cancer. Because of the incidence and demographics of breast cancer, hundreds of thousands of breast cancer patients throughout the world will start AI therapy each year. As a class, AIs are generally safe drugs and are reasonably well tolerated by most patients. They have been studied in very large adjuvant trials with careful documentation of both toxicity and patient compliance. The majority of symptoms associated with aromatase inhibition are related to the profound estrogen deprivation that arises as a consequence of AI therapy. These symptoms include hot flashes, night sweats, vaginal dryness, and sexual dysfunction. A particular concern among postmenopausal women has been bone health, including osteopenia, osteoporosis, and osteoporotic fracture, all of which are accelerated by AIs. Serial monitoring of bone mineral density and supplemental calcium, vitamin D, and exercise are recommended to minimize loss of bone density in women receiving AI therapy. Given that AI treatment has become more common, clinical experience has suggested another unique musculoskeletal adverse effect of AI therapy: arthralgias. Arthralgia is defined as pain in one or more joints, and is distinguished from arthritis, which suggests inflammation of the joint caused by structural damage, infection, autoimmunity, or metabolic conditions. Arthralgias may be difficult to gauge in older breast cancer patients with early-stage breast cancer owing to the high prevalence of degenerative joint disease and of nonspecific muscle and joint aches. In patients with stage IV breast cancer, the frequency of bone metastases also confounds easy measurement of drug-associated musculoskeletal symptoms. The pivotal trials of AIs as treatment for metastatic breast cancer documented musculoskeletal complaints in 4% to 13% of women receiving AI therapy. The first report linking AI therapy to arthralgia was a letter to the editor of the Journal of Clinical Oncology (JCO) in 2001, describing a series of patients with metastatic breast cancer treated with AI therapy in the United Kingdom. Among 77 women starting AI therapy for metastatic breast cancer, 16% developed new joint pains, mostly of moderate grade, prompting one in four of these women to stop AI therapy because of symptoms. In each case, cessation of AI therapy resolved the joint pain. The most common sites of joint pain were hands, knees, hips, back, and shoulders. The seminal studies of adjuvant AI therapy such as the Arimidex, Tamoxifen, Alone or in Combination, Breast International Group 1-98, Intergroup Exemestane Study, and National Cancer Institute of Canada trial MA17, captured data on adverse effects of AI and tamoxifen as treatment-related toxicity. Bone and joint symptoms were categorized in a variety of different ways in the toxicity reporting of the major adjuvant AI trials, including classification as arthritis, joint pain, or musculoskeletal disorders, to name several examples. Estimates for the incidence of musculoskeletal problems in these trials range from 5% to 36% among patients receiving AI therapy, and from 4% to 29% among women taking tamoxifen. These broad ranges reflect both the variety of definitions of arthritis, arthralgia, and bone pain, and the potentially confounding effect of widely prevalent nonspecific bone symptoms. Formal surveys of patients continuing protocol treatment in the adjuvant AI trials showed comparable global quality of life among women taking either AI or tamoxifen/placebo therapies, suggesting that whatever musculoskeletal symptoms patients were experiencing, they had on average a modest impact on dayto-day function and activity. Nonetheless, growing clinical experience suggests that AI therapy can be associated with a particular clinical syndrome of arthralgias, and breast cancer specialists are becoming more familiar with these symptoms. Typical sites of discomfort include the hands (fingers and wrists), arms, knees, and feet, pelvic and hip bones, and back. In our experience, patients describe stiffness, achiness, or pain that is usually symmetric, may be associated with mild thickening of the soft tissues (for instance, rings do not fit as before), and is temporally associated with onset of AI therapy. Symptoms may be most noticeable on awakening, and often improve with morning activities. Patients frequently make gestures such as squeezing or flexing their joints to show affected areas, and often mention that they feel they have “aged” abruptly. The true prevalence of such symptoms among breast cancer survivors is not known, and thus the report from Crew et al at JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 25 NUMBER 25 SEPTEMBER 1 2007

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.455
Teacher spread0.376 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations72
Published2007
Admission routes1
Has abstractyes

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