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Record W3208075057 · doi:10.1093/jncics/pkab087

Predictors of Pain Reduction in Trials of Interventions for Aromatase Inhibitor–Associated Musculoskeletal Symptoms

2021· article· en· W3208075057 on OpenAlex
N. Lynn Henry, Joseph M. Unger, Cathee Till, Katherine D. Crew, Michael Fisch, Dawn L. Hershman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJNCI Cancer Spectrum · 2021
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicinePhysical therapyConfidence intervalOdds ratioInternal medicineOsteoarthritisRandomized controlled trialAromatase inhibitorBreast cancerCancerAlternative medicineTamoxifen

Abstract

fetched live from OpenAlex

Abstract Background Almost one-half of aromatase inhibitor (AI)–treated breast cancer patients experience AI-associated musculoskeletal symptoms (AIMSS); 20%-30% discontinue treatment because of severe symptoms. We hypothesized that we could identify predictors of pain reduction in AIMSS intervention trials by combining data from previously conducted trials. Methods We pooled patient-level data from 3 randomized trials testing interventions (omega-3 fatty acids, acupuncture, and duloxetine) for AIMSS that had similar eligibility criteria and the same patient-reported outcome measures. Only patients with a baseline Brief Pain Inventory average pain score of at least 4 of 10 were included. The primary outcome examined was 2-point reduction in average pain from baseline to week 12. Variable cut-point selection and logistic regression were used. Risk models were built by summing the number of factors statistically significantly associated with pain reduction. Analyses were stratified by study and adjusted for treatment arm. Results For the 583 analyzed patients, the 4 factors statistically significantly associated with pain reduction were Functional Assessment of Cancer Therapy Functional Well-Being greater than 24 and Physical Well-Being greater than 14 (higher scores reflect better function), and Western Ontario and McMaster Universities Osteoarthritis Index less than 50 and Modified Score for the Assessment and Quantification of Chronic Rheumatoid Affections of the Hands less than 33 (lower scores reflect less pain). Patients with all 4 factors were greater than 6 times more likely to experience at least a 2-point pain reduction (odds ratio = 6.37, 95% confidence interval = 2.31 to 17.53, 2-sided P < .001); similar results were found for secondary 30% and 50% pain reduction endpoints. Conclusions Patients with AIMSS who have lower symptom and functional distress at study entry on AIMSS intervention trials are more likely to experience meaningful pain reduction. Baseline symptom and functional status should be considered as stratification factors in future interventional trials.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.153
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.379
Teacher spread0.339 · 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