Is Positron Emission Tomography an Accurate Non-invasive Alternative to Sentinel Lymph Node Biopsy in Breast Cancer Patients?
Bibliographic record
Abstract
Breast cancer is the most common malignancy among women, leading to approximately 45 000 deaths per annum in the United States (1). The presence of axillary lymph node metastases has major prognostic implications in breast cancer patients (2,3), and it is an important criterion in determining the need for adjuvant chemotherapy (4). Sentinel lymph node (SLN) biopsy has become routine practice in the surgical treatment of patients with breast cancer because the disease status of the SLNs accurately reflects the status of the remaining axillary lymph nodes (5–7). Hence, patients who present with a negative SLN (no metastases) can be spared from having a more traumatic axillary lymph node dissection (ALND), which has been shown to be associated with substantial short- and long-term sequelae (8,9). Despite the enormous advantages of SLN biopsy over ALND in regard to post-operative complications in node-negative patients, it would be of great clinical benefit if a reliable non-invasive method to assess lymph node status in breast cancer patients could be found.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".