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Abstract P3-15-05: Muscle and Joint Symptoms in Breast Cancer Patients Receiving Taxane-Based Chemotherapy

2010· article· en· W2093577168 on OpenAlexaff
Jenna van Draanen, Erica Stacey, R. Dent, Daniela Gallo-Hershberg, Mark Pasetka, Angie Giotis, Kan K, Leah van Draanen, Tielan Fang, V Lee, Susan Walker, Carlo DeAngelis

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsHealth Sciences CentreNorth York General HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineTaxaneBreast cancerInternal medicineCancerDocetaxelJoint painChemotherapyPaclitaxelOncologyPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Background: Docetaxel, paclitaxel, and (nab)-paclitaxel are taxanes used in treating breast cancer at various stages. While fairly well tolerated, they can cause distressing side effects. Both the prevalence and severity of muscle and joint symptoms (M&JS) in patients receiving taxane-based chemotherapies are poorly documented. We prospectively investigated the prevalence and severity of M&JS in women receiving taxane chemotherapy for breast cancer. Methods: A total of 275 taxane-naive patients were enrolled in the study. This analysis presents data for 95 patients. For three consecutive treatmentcycles patients completed a baseline questionnaire as well as a diary, days 1 through 7 and on days 14 and 21 following treatment. Patient interviews were done by telephone days 2 to 3, and days 5 to 7 following treatment. They were then contacted at approximately three month intervals for one year. Data collection and data entry are ongoing; long term follow-up will conclude in August 2010. Results: The average age of patients was 53 years. Disease status was classified as early for 50%, locally advanced for 35.34 %, and metastatic for 15.51% of patients. Overall 41.4% and 39.6% of patients reported muscle and joint pain, respectively, in their diaries on days 1 to 7. Muscle pain was most commonly reported in the legs, back, and arms and joint pain in the knees, ankles, and hips. The pain was most often described as aching, tiring, nagging, exhausting, and tender. The following scores are derived from a 10 cm visual analog scale. Of those who experienced pain in the seven days after treatment, the mean rating for pain at its worst was 5.15 and 5.02 for muscle and joint pain, respectively. Pain at its least had an average rating of 3.42 and 3.41 for muscle and joint pain, respectively. With respect to interference, patients who experienced pain had a mean score of 5.39, 4.86, 5.5, 4.12, 5.36, and 5.44 for interference with basic activities in daily life, mood, working or housework, relationships, sleeping and enjoyment of life, respectively. One, 3, 6, 9 and 12 months after taxane treatment ended, 27.2% (22/81), 31% (13/42), 45.5% (20/44), 37.2% (16/43), and 26.9% (7/26) reported muscle pain, respectively. Joint pain was reported by 26% (21/81), 38.1% (16/42), 43.2% (19/44), 48.8% (21/43) and 34.6% (9/26) of patients 1, 3, 6, 9 and 12 months following treatment, respectively. Data entry and analysis is ongoing and the final analysis will report on pain in relation to the taxane used, steroid tapering, role of anti-hormonal therapy, and effect of menopause on incidence of M&JS. Discussion: Our preliminary data analysis indicates incidence rates of M&JS higher than previously reported during taxane treatment, and persistence of symptoms in a large proportion of patients up to a year following therapy completion. High pain scores and considerable interference with many aspects of life were reported. This study confirms the significant burden of M&JS during taxane chemotherapy. Conclusions: M&JS during taxane treatment is a common occurrence, and is significantly debilitating to the women who experience it. More research should be done regarding preventative measures and treatment options. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-15-05.

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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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.045
GPT teacher head0.404
Teacher spread0.358 · 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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Citations1
Published2010
Admission routes1
Has abstractyes

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