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Record W2583482683 · doi:10.1001/jamaoto.2016.3669

Survival Rates Using Individualized Bioselection Treatment Methods in Patients With Advanced Laryngeal Cancer

2017· article· en· W2583482683 on OpenAlexaff
Gregory T. Wolf, Emily L. Bellile, Avraham Eisbruch, Susan Urba, Carol R. Bradford, Lisa A. Peterson, Mark E. Prince, Theodoros N. Teknos, Douglas B. Chepeha, Norman D. Hogikyan, Scott A. McLean, Jeffery Moyer, Jeremy M. G. Taylor, Francis P. Worden

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedicineChemoradiotherapyLaryngectomyRadiation therapyOncologyChemotherapySurgeryInternal medicineCancerLarynx

Abstract

fetched live from OpenAlex

Importance: Use of chemoradiotherapy for advanced laryngeal cancer led to a major shift in treatment as an alternative to laryngectomy. Despite widespread adoption of chemoradiotherapy, survival rates have not improved and the original premise of matching neoadjuvant chemotherapy tumor response to determine subsequent treatment has not been followed. Objective: To determine whether improved survival could be achieved by incorporating a single cycle of neoadjuvant chemotherapy to select patients with advanced disease for either laryngectomy or concurrent chemoradiotherapy. Design, Setting, and Participants: An unselected cohort of 247 patients with laryngeal cancer in an academic institution between 2002 and 2012 was evaluated. Patients with limited disease (stages I and II) underwent endoscopic resection, radiotherapy, or chemoradiotherapy for deeply invasive T2 lesions. For patients with advanced disease (stages III and IV), neoadjuvant chemotherapy, concurrent chemoradiotherapy, or primary surgery was recommended. Overall survival (OS) and disease-specific survival (DSS) were analyzed. Median follow-up was 48 months. The study was conducted from January 1, 2002, to December 31, 2012; data analysis was completed December 1, 2015. Interventions: Endoscopic resection, radiotherapy, chemoradiotherapy, neoadjuvant chemotherapy, concurrent chemoradiotherapy, and primary surgery. Main Outcomes and Measures: Overall survival and DSS. Results: Of the 247 patients, 191 (77.3%) were male; mean (SD) age was 59.6 (10.4) years. Of 94 patients with limited disease, 33 (35.1%) underwent endoscopic resection; 50 (53.2%), radiotherapy alone; and 11 (11.7%), chemoradiotherapy for deeply invasive T2 lesions. Of 153 patients with advanced disease, 71 (46.4%) received neoadjuvant chemotherapy; 50 (32.7%), concurrent chemoradiotherapy; and 32 (20.9%), surgery. Five-year OS and DSS was 75% (95% CI, 68%-81%) and 83% (95% CI, 77%-88%), respectively, for the entire cohort. The DSS was 92% (95% CI, 83%-97%) for patients with stage I or II and 78% (95% CI, 69%-84%) for patients with stage III or IV disease. For patients with advanced disease, 5-year OS and DSS ranged from 78% (95% CI, 55%-90%) and 91% (95% CI, 67%-98%), respectively, for surgery; to 76% (95% CI, 63%-85%) and 79% (95% CI, 67%-88%), respectively, for neoadjuvant bioselection; and to 61% (95% CI, 44%-75%) and 66% (95% CI, 48%-79%), respectively, for primary chemoradiotherapy. Propensity-adjusted, multivariable controlling for known prognostic factors DSS was significantly improved in the neoadjuvant group compared with the chemoradiotherapy group (hazard ratio [HR], 0.48; 95% CI, 0.29-0.80). Conclusions and Relevance: Superior survival rates were achieved with a bioselective treatment approach using a single cycle of neoadjuvant chemotherapy. Good survival rates were also achieved in patients selected for primary surgery, and both neoadjuvant chemotherapy and primary surgery were better than survival rates with concurrent chemoradiotherapy, suggesting that the optimal individualized treatment approach for patients with advanced laryngeal cancer has not yet been defined.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.060
GPT teacher head0.395
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 teacher head, not a consensus.

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

Citations43
Published2017
Admission routes1
Has abstractyes

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