Neurological complications of breast cancer: A prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Neurological complications secondary to breast cancer treatment may be an important contributor to these patients morbidity. We aimed to quantify the incidence of neurological complications of breast cancer treatment during the first year after diagnosis. MATERIALS AND METHODS: We performed a prospective cohort study with 506 patients recruited at the Portuguese Institute of Oncology of Porto, among those newly diagnosed. Participants underwent a neurological examination before treatment, after surgery, after chemotherapy (whenever applicable) and at one year after enrollment. The Montreal Cognitive Assessment was used to assess cognitive function, at baseline and at one year. We computed one-year cumulative incidence estimates and the corresponding 95% confidence intervals (95%CI) for each of the neurological complications. RESULTS: Just over half of women had breast cancer stage 0 or I. A total of 6.9% were submitted to neoadjuvant chemotherapy but most of them completed adjuvant treatment - endocrine therapy, radiotherapy or chemotherapy (83.9%, 73.0% and 52.5%, respectively). The cumulative incidence of at least one oncological-related neurological complication during the first year after diagnosis was 48.4% (95%CI: 44.1-52.8); the most frequent were neuropathic pain (30.8%, 95%CI: 27.0-35.0), chemotherapy-induced peripheral neuropathy (16.8%, 95%CI: 13.8-20.3), phantom breast pain/syndrome (16.6%, 95%CI: 13.6-20.1) and cognitive decline (8.1%, 95%CI: 5.8-11.1). CONCLUSIONS: Neurological complications were a frequent side-effect of breast cancer management in the first year after diagnosis, especially neuropathic pain and chemotherapy-induced peripheral neuropathy. Accurate diagnosis and treatment of these complications are important to minimize the burden associated with breast cancer treatment in breast cancer survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".