MétaCan
Menu
Back to cohort
Record W2159807872 · doi:10.1136/jcp.2010.086405

Melphalan as a treatment for <i>BRCA</i> -related ovarian carcinoma: can you teach an old drug new tricks?: Figure 1

2011· article· en· W2159807872 on OpenAlexaff
Deidra J Osher, Yaël B. Kushner, Jocelyne Arseneau, William D. Foulkes

Bibliographic record

VenueJournal of Clinical Pathology · 2011
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMelphalanOlaparibBRCA mutationCisplatinOvarian cancerOncologyMedicineChemotherapyOvarian carcinomaInternal medicineCancer researchDrug resistanceCancerBiologyGeneticsPoly ADP ribose polymeraseDNA

Abstract

fetched live from OpenAlex

Late-stage ovarian carcinoma is almost universally fatal. BRCA mutations are associated with an improved outcome and enhanced sensitivity to platinum chemotherapy, yet recurrence and platinum resistance remain a major problem and highly effective regimens following platinum failure do not yet exist. Here we report a remarkable case of cure following platinum-resistant stage III ovarian carcinoma in a woman with a BRCA2 mutation. The patient was subsequently treated with oral melphalan therapy and has not recurred in over 25 years. Melphalan is a bifunctional alkylator that creates inter- and intra-strand DNA cross-links. In a pharmaceutical screen, melphalan was shown to be selectively toxic to BRCA2-deficient breast cancer cell lines and produced a longer relapse-free survival in mice than did cisplatin or olaparib. There is increasing evidence to consider BRCA mutation status when selecting chemotherapy regimens, and melphalan treatment for BRCA-related ovarian cancer merits further investigation. Focusing attention on long-term survivors may provide new mechanistic insights into the biology of chemo-responsiveness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.140
GPT teacher head0.419
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical PathologySame topicPARP inhibition in cancer therapyFrench-language works237,207