Arthralgia Associated with Autoimmune Abnormalities under Aromatase Inhibitor Therapy: Outcome after Cessation of Treatment
Bibliographic record
Abstract
To the Editor: Arthralgia is a frequent occurrence in patients treated with aromatase inhibitors (AI) for breast cancer. The joint pain is sometimes invalidating and affects treatment compliance1. In 2007, we reported a series of 23 patients presenting with arthralgia, sometimes with arthritis, associated with sicca syndrome and with autoimmune abnormalities [antinuclear antibodies (ANA), rheumatoid factor (RF), antithyroglobulin antibodies (ATGA)]2. We wished to review these patients after the cessation of AI treatment to determine whether the joint pain persisted and to observe the outcome of the immunological abnormalities. Twenty-three patients with arthralgia were contacted who had received AI therapy for 5 years (only 17 patients were reviewed in our current study: anastrozole 7, exemestane 5, letrozole 5) and who had, at the initial consultation, either ANA above 1:320 or RF or ANA above 1:160 associated with ATGA. These women had discontinued their AI treatment for at least 1 year. The patients underwent a detailed interview and rheumatological … Address correspondence to Professor M. Laroche, Centre de Rhumatologie, Hôpital Pierre-Paul Riquet, 1 place du Dr Baylac, 31059 Toulouse Cedex, France. E-mail: laroche.m{at}chu-toulouse.fr
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".