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Record W4323535383 · doi:10.1097/coc.0000000000000992

Population-based Long-term Outcomes for Squamous Cell Carcinoma of the Nasal Cavity

2023· article· en· W4323535383 on OpenAlexaff
Sarah Hamilton, Jason Liu, Connor Holmes, Kimberly DeVries, Robert Olson, Eric Tran, Eric Berthelet, Jonn Wu, Nicole G. Chau, Matthew Chan, Andrew Thamboo

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsPositive Living NorthUniversity of British Columbia
Fundersnot available
KeywordsMedicineNasal cavitySurgeryHazard ratioRadiation therapyRetrospective cohort studyPopulationBasal cellAdjuvant radiotherapyProportional hazards modelInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: This study evaluates population-based outcomes of patients with squamous cell carcinoma (SCC) of the nasal cavity treated in British Columbia. METHODS: A retrospective review of nasal cavity SCC treated from 1984 to 2014 was performed (n = 159). Locoregional recurrence (LRR) and overall survival (OS) were evaluated. RESULTS: The 3-year OS was 74.2% for radiation alone, 75.8% for surgery alone, and 78.4% for surgery and radiation ( P = 0.16). The 3-year LRR was 28.4% for radiation alone, 28.2% for surgery alone, and 22.6% for surgery and radiation ( P = 0.21). On multivariable analysis, surgery and postoperative radiation relative to surgery alone was associated with a lower risk of LRR (hazard ratio: 0.36, P = 0.03). Poor Eastern Cooperative Oncology Group status, node-positive, orbital invasion, smoking, and advanced age were associated with worse OS (all P <0.05). CONCLUSION: In this population-based analysis, multimodality treatment with surgery and adjuvant radiation were associated with improved locoregional control for SCC of the nasal cavity.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.104
GPT teacher head0.464
Teacher spread0.360 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Clinical OncologySame topicHead and Neck Surgical OncologyFrench-language works237,207