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Record W4408186699 · doi:10.1001/jamaoto.2024.3738

Lung Cancer Surveillance for Patients With Head and Neck Cancer

2025· article· en· W4408186699 on OpenAlexaffabout
Naif Fnais, Francisco Laxague, Marco A. Mascarella, Raisa Chowdhury, Hedi Zhao, Sukhdeep Jatana, Abrar Aljassim, Catherine F. Roy, Abdulaziz Al‐Rasheed, David S. Chan, Jason Agulnik, Reza Forghani, Khalil Sultanem, Alex Mlynarek, Michael Hier

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsWestern UniversityMcGill University Health CentreUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsCancerHead and neck cancerMedicineLung cancerHead and neckOncologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Importance: Patients with head and neck squamous cell cancer (HNSCC) are at a greater risk of developing pulmonary metastases and/or second primary lung cancer. However, it remains uncertain whether lung screening in these patients, when the initial staging studies are negative, confers any survival benefit. Objective: To evaluate long-term cancer survival outcomes in patients with HNSCC undergoing chest radiography vs low-dose computed tomography screening for pulmonary metastasis and/or second primary lung cancer. Design, Setting, and Participants: This randomized parallel trial was conducted at a large academic hospital in Canada enrolling treatment-naive patients with de novo HNSCC from September 2015 to December 2022. Eligible patients did not meet the criteria for lung screening established by the US National Comprehensive Cancer Network guidelines. Participants were randomized to chest radiography or low-dose computed tomography screening groups. Data were analyzed from March to August 2024. Intervention or Exposure: Comparison of chest radiography vs low-dose computed tomography screening methods. Main Outcomes and Measures: Primary outcomes were the lung cancer detection rate measured by comparing the sensitivity and specificity of low-dose computed tomography with chest radiography. Secondary outcomes were overall survival and disease-free survival. Results: A total of 137 patients (mean [SD] age, 65.1 [14.1] years; 34 [24.8%] females and 103 [75.2%] males) were included and randomized, 68 (49.6%) to chest radiography and 69 (50.4%) to low-dose computed tomography. Nine of 137 patients (6.5%) developed a second primary lung cancer (6 patients) or lung metastases (3 patients). There were no clinically meaningful differences in survival outcomes between the 2 groups (hazard ratio, 1.2; 95% CI, 0.4-3.9). Chest radiography exhibited a relatively low sensitivity of 66.7% but a specificity of 100%. Low-dose computed tomography demonstrated both high sensitivity (100%) and specificity (100%), for an overall accuracy of 100%. Conclusions and Relevance: The findings of this randomized parallel trial indicate that low-dose computed tomography exhibits statistically significant superior sensitivity compared with chest radiography for diagnosing lung metastases and second primary lung cancer. However, there were no important differences in survival rates. These results hold practical significance, offering valuable insights to clinicians who are guiding decisions regarding lung screening protocols. Trial Registration: ISRCTN10954990.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.299
Teacher spread0.285 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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