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Record W4214523808 · doi:10.3747/co.23.3085

Did the Addition of Concomitant Chemotherapy to Radiotherapy Improve Outcomes in Hypopharyngeal Cancer? A Population-Based Study

2016· article· en· W4214523808 on OpenAlexafffundvenueabout
Stephen F. Hall, Rebecca Griffiths

Bibliographic record

VenueCurrent Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersOntario Ministry of Health and Long-Term CareCancer Care Ontario
KeywordsMedicineRadiation therapyHead and neck cancerHypopharyngeal cancerInternal medicineConcomitantIncidence (geometry)PopulationHead and neckOncologyCancerRetrospective cohort studyCohortSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: For oncologists and for patients, no site-specific clinical trial evidence has emerged for the use of concurrent chemotherapy with radiotherapy (ccrt) over radiotherapy (rt) alone for cancer of the hypopharynx (hpc) or for other human papilloma virus-negative head-and-neck cancers. METHODS: This retrospective population-based cohort study using administrative data compared treatments over time (1990-2000 vs. 2000-2010), treatment outcomes, and outcomes over time in 1333 cases of hpc diagnosed in Ontario between January 1990 and December 2010. RESULTS: The incidence of hpc is declining; the use of ccrt that began in 2001 is increasing; and the 3-year overall survival for all patients remains poor at 34.6%. No difference in overall survival was observed in a comparison of patients treated in the decade before ccrt and of patients treated in the decade during the uptake of ccrt. CONCLUSIONS: The addition of ccrt to the armamentarium of treatment options for oncologists treating head-and-neck patients did not improve outcomes for hpc at the population level.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.425
Teacher spread0.358 · 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

Citations19
Published2016
Admission routes4
Has abstractyes

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