Breast Cancer – Diagnosis and Treatment Prolonging Life: A Review
Bibliographic record
Abstract
Breast cancer is a malignant tumour that starts either in the ducts or lobules, this can be generally differentiated as either in situ or invasive (in filtering) type. It is expected that in 2014 every 1 in 8 women are likely to develop invasive breast cancer during their lifetime when compared to a decade back where an average of 1 in 10 was seen. With this increase, breast cancer alone, roughly accounts for 25 to 30% of new cancer cases this year. Despite such diagnostic statistics, there are millions of survivors across the globe and this increasing rate can be attributed to the tremendous increase in advances in treatment and also early diagnosis. New drug delivery carriers like nanoparticles, liposomes, monoclonal antibodies, etc. are being used to improve the efficacy of therapy and for site specific delivery to reduce side effects. As a result of the constant effort by researchers, today the commercial market has a range of products apart from the conventional dosage forms like Herceptin (trastuzumab), a monoclonal antibody; the others in this class are Pertuzumab (Perjeta), Kadcyla (ado-trastuzumab emtansine) used for targeted delivery; Myocet (doxorubicin), a liposomal formulation and Paclitaxel nanoparticles all these are available as injections via intravenous route or infusion in few cases. There are yet certain other promising technologies like magnetic nanoparticle hyperthermia and cMethDNA assay a very assuring method to monitor recurrence of breast cancer by a simple blood test. This review will focus on the description of disease, diagnosis, current treatment therapies and ongoing research to provide better facilities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".