A prospective study on the neurological complications of breast cancer and its treatment: Updated analysis three years after cancer diagnosis
Bibliographic record
Abstract
OBJECTIVES: To quantify the prevalence of neurological complications among breast cancer patients at one and three years after diagnosis, and to identify factors associated with neuropathic pain (NP) and chemotherapy-induced peripheral neuropathy (CIPN). MATERIAL AND METHODS: Prospective cohort study including 475 patients with newly diagnosed breast cancer, recruited among those proposed for surgical treatment (Portuguese Institute of Oncology, Porto). Patients underwent a neurological evaluation and had their cognitive function assesses with the Montreal Cognitive Assessment, before treatment and at one and three years after enrollment. We estimated the prevalence of each neurological complication, and odds ratios (OR), adjusted for socio-demographic and clinical characteristics, to identify factors associated with NP and CIPN. RESULTS: More than half of the patients [54.7%, 95% confidence interval (95%CI): 50.2-59.2] presented at least one neurological complication, at one or at three years after cancer diagnosis. Between the first and the third year of follow-up, there was an increase in the prevalence of NP (from 21.1% to 23.6%), cognitive impairment (from 7.2% to 8.2%), cerebrovascular disease (from 0.6% to 1.5%) and brain metastasis (from 0.0% to 0.6%). The prevalence of CIPN decreased from 14.1% to 12.6%. Axillary lymph node dissection was associated with NP at one year (OR = 2.75, 95%CI: 1.34-5.63) and chemotherapy with NP at three years (OR = 2.10, 95%CI: 1.20-3.67). Taxane-based chemotherapy was strongly associated with prevalence of CIPN at one and three years. CONCLUSION: Neurological complications are frequent even three years after cancer diagnosis and NP remained the major contributor to the burden of these conditions among 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".