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Record W2112044434 · doi:10.1002/jso.21002

Are buccal cancers in India and Canada any different?

2008· article· en· W2112044434 on OpenAlexaffabout
Kumar Alok Pathak, Richard W. Nason, Sanjay Talole, A. Abdoh

Bibliographic record

VenueJournal of Surgical Oncology · 2008
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsMedicineProportional hazards modelConfoundingBuccal mucosaInternal medicineHazard ratioSurvival analysisLog-rank testGastroenterologySignificant differenceBuccal administrationSurgeryDentistryConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: To compare the treatment outcomes of squamous cell carcinoma (SCC) of buccal mucosa in India and Canada. METHODS: We compared the outcome of 169 patients with SCC of buccal mucosa treated at Tata Memorial Hospital (TMH), India with 64 matched patients from Cancer Care Manitoba (CCMB), Canada. Overall and cause specific survivals for the two geographical groups were calculated by Kaplan-Meir method and compared using log rank test. Cox regression analysis was used to see impact of independent variables. RESULTS: At 5 years, CCMB patients had lower over all survival (57.4% vs. 67.1%; P = 0.002) than TMH ones but similar cause specific survival (76.4% vs. 74.2%; P = 0.690). Age had an independent influence on both over all and cause specific survival. After adjusting for the age confounding in the Cox proportional hazard model there was no difference in the overall survival of the two groups (HR = 0.84; 95% CI = 0.51, 1.40; P = 0.509). Radiated patients had three times higher risk of dying of disease than surgically treated ones (HR = 3.03; 95% CI = 1.64, 5.60; P < 0.001). CONCLUSIONS: There was no difference in the cause specific survival between the two groups. Apparent difference in the overall survival was due to the difference in the age of presentation.

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 score0.226
Threshold uncertainty score0.976

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 head0.029
GPT teacher head0.308
Teacher spread0.279 · 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

Citations7
Published2008
Admission routes2
Has abstractyes

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