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Record W2909995331 · doi:10.14740/jmc.v10i1.3172

Metachronous and Synchronous Multiple Primary Carcinomas in an Elderly

2019· article· en· W2909995331 on OpenAlexvenueno aff
Ranim R. Mira, Andrea Bial, Kellie Hunter Campbell, Salman Ali, Shahad Abdulameer, Martin J. Gorbien

Bibliographic record

VenueJournal of Medical Cases · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerEpiglottisPopulationCarcinomaPrimary tumorInternal medicineOncologyLarynxSurgeryMetastasis

Abstract

fetched live from OpenAlex

The prognosis of most cancers has been improved in recent years due to the increased survival of cancer patients, the prolonged lifespan of the general population, and better diagnostic and surgical approaches. Subsequently, the number of patients with multiple primary carcinomas (MPCs) has become greater. In this report, we describe a unique case of a 71-year-old man with five metachronous and synchronous primary malignant tumors. The patient was first diagnosed with synchronous, left posterior tonsillar pillar squamous cell carcinoma (SCC) and right lingual surface of epiglottis SCC in June 2016. Two years later, he presented with three other primary carcinomas within a 3-month time span: right lower lobe lung SCC, right tongue invasive SCC and hepatocellular carcinoma (HCC), consecutively. Investigations revealed no metastases of the primary neoplasms. As the population of older adults with cancer and multimorbidity grows, the therapeutic options usually become limited. On the other hand, understanding the effect of multimorbidity on the care of patients with cancer and developing therapeutic interventions for these elderly patients would be crucial for geriatric care. J Med Cases. 2019;10(1):8-13 doi: https://doi.org/10.14740/jmc3172

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.022
GPT teacher head0.290
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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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