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Efficacy of the novel treatments of BCC with bone metastasis.

2022· article· en· W4286298041 on OpenAlexaff
Mohammad Ali Esmaeil Pour, Mahsa Mohebtash, Ali Amin, Azadeh Khayyat, Amir-Reza Khalili-Toosi, S Mousavi, Mengni Guo, Jieying Liu

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsFraser HealthProvidence Health Care
Fundersnot available
KeywordsVismodegibMedicineBasal cell carcinomaRadiation therapyMetastasisOncologyDermatologyInternal medicineChemotherapyAdverse effectBasal cellCancer

Abstract

fetched live from OpenAlex

e14547 Background: Eighty percent of nonmelanoma skin malignancies are basal cell carcinomas (BCC). Metastasis in BCC is about 0.0028 to 0.55. Diagnosis and treatment of metastatic BCC is challenging due to its rarity. In approximately 85% of metastatic BCC, the most common metastatic organs are lymph nodes. Methods: We reviewed 31 skin BCCs with bone metastasis in the past 60 years and 10 observation studies on the efficacy of Sonic hedgehog inhibitors (SHHis). We focused on treatment and outcome of 31 metastatic BCC. Results: The main treatment option was surgery and radiotherapy is a palliative option. Among chemotherapeutic and immunotherapeutic agents, SHHis (e.g. vismodegib and sonidegib) appear to be effective, with reported ORR of 43-100%, and CRR range of 3%-54%. In locally advanced BCCs, vismodegib shows 10 times higher CRRs compared to sonidegib, although ORRs were similar. In metastatic BCC, overall response to vismodegib was three times higher. The common adverse effects of SHHis limit their administration. Conclusions: BCC is a common skin neoplasm that rarely demonstrates metastasis. Among the chemotherapy agents administered, vismodegib appears promising.[Table: see text]

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

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

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 head0.118
GPT teacher head0.423
Teacher spread0.305 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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