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Record W3033396031 · doi:10.21037/apm-20-258

Medically inoperable Merkel cell carcinoma of the head and neck treated with stereotactic body radiation therapy: a case report

2020· article· en· W3033396031 on OpenAlexaff
Jie Wei Zhu, Lilian Doerwald-Munoz, Justin Wann-Yee Lee

Bibliographic record

VenueAnnals of Palliative Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsJuravinski Cancer CentreMcMaster University
Fundersnot available
KeywordsMedicineMerkel cell carcinomaRadiation therapyMalignancySkin cancerSurgeryBiopsyMelanomaRadiosurgeryHead and neck cancerCarcinomaCancerRadiologyDermatologyInternal medicine

Abstract

fetched live from OpenAlex

Merkel cell carcinoma (MCC) is a rare but aggressive neuroendocrine tumour of the skin. MCC is the second most common cause of death from non-melanoma skin cancer and the most aggressive cutaneous malignancy. An 88-year-old male presented with a large, bleeding skin tumour located on the right temple and pre-auricular region. A biopsy confirmed MCC; immunohistochemistry (IHC) was positive for synaptophysin and CK20. The patient was assessed by a head and neck surgical oncologist and not deemed to have operable disease due to medical co-morbidities and extent of disease. The patient underwent a single fraction of electron treatment, followed by stereotactic body radiation therapy (SBRT) to a total dose of 40 Gy in 5 fractions over 2 weeks. Bleeding stopped and the patient tolerated treatment well with no reported side effects other than fatigue. There was symptomatic improvement within 2 weeks and a complete clinical response within 4 weeks of treatment. There are limited data on the use of radiotherapy in unresected/ inoperable MCC. For elderly, medically frail patients who cannot undergo surgery, SBRT may be an option to alleviate symptoms and control the tumour in a relatively short number of treatments; further study is warranted.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.029
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.065
GPT teacher head0.333
Teacher spread0.268 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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