Expert consensus recommendations on the daily clinical use of pembrolizumab for early triple-negative breast cancer
Bibliographic record
Abstract
Neoadjuvant chemotherapy is a standard treatment for triple-negative breast cancer (TNBC) at an early stage. Given that pathological complete response is strongly associated with long-term clinical and survival benefits, the selection of appropriate treatment before and after surgery could further optimise treatment outcomes. With the emergence of immunotherapy in breast cancer, more combination treatment options are available, such as pembrolizumab, a programmed death receptor 1 inhibitor, which is approved for the perioperative treatment of stage II and III TNBC. However, the implementation of immunotherapy in perioperative settings for TNBC requires further discussion regarding patient selection and the use of different treatments in conjunction with immunotherapy. The Hong Kong Breast Cancer Foundation convened a multidisciplinary consensus panel consisting of surgeons, clinical oncologists, and medical oncologists to initiate this discussion. A modified Delphi panel was conducted, evaluating seven topics and 45 statements covering the workup and perioperative treatment of early-stage TNBC (eTNBC). The consensus statements provide guidance on determining whether a patient with eTNBC is a suitable candidate for neoadjuvant chemotherapy and immunotherapy.
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 | 0.127 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".