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Record W2593041137 · doi:10.3747/co.24.3299

Nasopharyngeal Non-Intestinal-Type Adenocarcinoma: A Case Report and Updated Review of the Literature

2017· article· en· W2593041137 on OpenAlexaffvenue
Chirag Jain, Lisa Caulley, Kristian I. Macdonald, Bibianna Purgina, Chi Lai, B. Esche, Stephanie Johnson‐Obaseki

Bibliographic record

VenueCurrent Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineMalignancyAdenocarcinomaConcomitantPathologyDifferential diagnosisNasopharyngeal carcinomaRadiation therapyInternal medicineGastroenterologyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Non-intestinal-type adenocarcinoma is a malignancy traditionally found in the sinonasal cavity. To our knowledge, this case is the first reported of this rare condition originating in the nasopharynx. CASE PRESENTATION: A 67-year-old woman with nasopharyngeal non-intestinal-type adenocarcinoma, with an accompanying parapharyngeal mass received primary radiation treatment for both lesions. Her tumour subsequently persisted, with a concomitant conversion in pathology from a low- to a high-grade malignancy. RESULTS: Non-intestinal-type and intestinal-type adenocarcinomas of the nasopharynx are extremely rare tumours and do not appear in the World Health Organization classification system. We review the pathophysiologic features of these malignancies and propose modifications to the current classification system. CONCLUSIONS: Non-intestinal-type adenocarcinoma should be included in the differential diagnosis of nasopharyngeal masses. In our experience, this tumour in this location showed a partial response to primary radiation but later converted from a low- to a high-grade adenocarcinoma.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.425
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreReview

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

Citations8
Published2017
Admission routes2
Has abstractyes

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