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Record W2963189355 · doi:10.1186/s40463-019-0357-4

The effect of second hand smoke in patients with squamous cell carcinoma of the head and neck

2019· article· en· W2963189355 on OpenAlexaff
Sherif Idris, Abdulsalam Baqays, André Isaac, Jason Chau, Karen H. Calhoun, Hadi Seikaly

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of CalgaryUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineMalignancyInternal medicineProportional hazards modelHead and neck cancerHead and neck squamous-cell carcinomaProspective cohort studyRisk factorOncologyMultivariate analysisTobacco smokeLogistic regressionCancerBasal cellHead and neckHazard ratioCohortSurgeryConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Active tobacco smoking is a well-known risk factor for head and neck malignancy, and strong evidence has associated tobacco as the main carcinogenic factor in squamous cell cancers of this region. Evidence supporting a carcinogenic effect of second-hand smoke (SHS) on head and neck organs in non-smokers was also demonstrated with results consistent with those for active smokers. There is little data on the effects of SHS in patients previously treated for squamous cell carcinomas of the head and neck. OBJECTIVE: The purpose of this study was to prospectively evaluate the role of SHS on recurrence and survival in treated head and neck cancer patients. METHODS: We conducted a prospective cohort study to examine the association between self-reported SHS exposure and the risk of recurrence and mortality in patients treated for squamous cell cancers of the head and neck in a longitudinal fashion. Patients filled out an exhaustive smoking questionnaire on presentation and abbreviated questionnaires at each follow-up visit, which occurred every 6 months. Primary outcome measures were recurrence, development of a second primary malignancy, and recurrence-free survival. Chi square analysis was used to assess the association between SHS and the primary outcomes. A multivariate binary logistic regression analysis was applied to determine the independent predictors of recurrence. Cox proportional hazards and Kaplan Meier modeling were employed to assess the possible relationships between SHS exposure and time to develop the primary outcomes. RESULTS: Untreated new patients with a histologically confirmed diagnosis of first primary SCC of the UADT (defined as cancer of the oral cavity, the oropharynx, the hypopharynx, and the larynx) were recruited. Patients seen at The University of Texas Medical Branch (UTMB) Head and Neck oncology clinic from 1988 to 1996 were considered as cases in this study. One hundred and thirty-five patients were enrolled in the study. The median follow-up time for the sample was 54 months (3.92 years). Complete records were achieved for 92% of patients, thus 124 patients were included in the final analysis. SHS significantly correlated with recurrence and recurrence-free survival. The rate of recurrence was 46% in the group exposed to SHS and 22% in the non-exposed group. Based on multivariate binary logistic regression analysis, SHS exposure was detected as a significant independent predictor for recurrence (HR = 3.00 [95% CI 1.18-7.63]). Kaplan-Meier analysis demonstrated that patients who were not exposed to SHS had a statistically significant longer recurrence-free survival (log-rank P = 0.029). The mean survival for non SHS-exposed patients was 76 [63-89] months versus 54 [45-63] months for those exposed to SHS. CONCLUSIONS: SHS exposure is an independent predictor of recurrence and survival after head and neck cancer treatment. These results support the importance and efforts of reducing smoking at home in in the work-place.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.238
Teacher spread0.229 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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