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Record W3013533566 · doi:10.1002/hed.26147

Neoadjuvant chemotherapy followed by surgery for <scp>HPV</scp>‐associated locoregionally advanced oropharynx cancer

2020· article· en· W3013533566 on OpenAlexaff
Nader Sadeghi, Marco A. Mascarella, Sarah Khalifé, Agnihotram V. Ramanakumar, Keith Richardson, Arjun S. Joshi, Reza Taheri, Andrew Fuson, Nathaniel Bouganim, Robert D. Siegel

Bibliographic record

VenueHead & Neck · 2020
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineInternal medicineChemotherapyCohortOncologyStage (stratigraphy)Prospective cohort studyCancerSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Neoadjuvant chemotherapy followed by surgery (NAC + S), a paradigm based on systemic escalation coupled with surgery-based de-escalation, is under investigation for treatment of HPV-associated oropharynx cancer (OPC). METHODS: Prospective cohort of patients with non-metastatic, p16 positive OPC enrolled in a clinical trial of NAC + S was compared to a historic cohort of patients undergoing concurrent chemoradiation (CCRT) to compare disease-free survival (DFS). RESULTS: Fifty-five patients were treated with NAC + S and 142 with CCRT. Stage-matched patients undergoing CCRT had higher frequency of smoking and alcohol consumption. 5-year DFS in the NAC + S group was 96.1% (95% CI 90.8-100) compared to 67.6% (95% CI 50.7-84.5) for CCRT (P = .01). At 12 months from treatment, 24.5% of patients undergoing CCRT and none of the patients in the NAC + S were feeding tube dependent (P < .0001). CONCLUSION: NAC + S may be a novel approach for HPV-associated OPC as it provides lower feeding tube dependence and improved survival compared to stage-matched patients undergoing CCRT.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.037
GPT teacher head0.311
Teacher spread0.274 · 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

Citations41
Published2020
Admission routes1
Has abstractyes

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