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Record W4414493962 · doi:10.1001/jamaoto.2025.2687

Palliative Care With Tracheostomy or Gastrostomy Tube Use and End-of-Life Quality and Costs Among Patients With Head and Neck Cancer

2025· article· en· W4414493962 on OpenAlexaffabout
Rui Fu, Rinku Sutradhar, Li Q, Noémie Villemure‐Poliquin, Kelvin Chan, Irene Karam, Julie Hallet, Antoine Eskander

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsHealth Sciences CentrePublic Health OntarioUniversity of TorontoUniversity of CalgaryInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsHead and neck cancerGastrostomyGastrostomy tubePalliative careQuality of life (healthcare)Tracheostomy tubeCohortPopulationHealth care

Abstract

fetched live from OpenAlex

Importance: Patients with head and neck cancer (HNC) have high utilization rates of tracheostomy or gastrostomy tubes (g-tubes) at the end of life, with accompanying high costs. It is unknown whether the timing of palliative care (PC) initiation may attenuate the cost or be associated with better quality of life during the last year and more home deaths. Objective: To assess the association of palliative care (first exposure) and tracheostomy or g-tube utilization with end-of-life costs among patients with head and neck cancer during the last year of life. Design, Setting, and Population: This was a population-based cohort study of adults diagnosed with HNC between January 1, 2007, and December 31, 2022, who died before October 1, 2023, in Ontario, Canada. Health administrative data were deterministically linked and analyzed at the ICES (formerly Institute for Clinical Evaluative Sciences). Data analysis was conducted from January 2024 to June 2025. Exposures: Timing of PC, categorized as early (12 to 6 months before death), late (<6 months before death), and none (no PC during last year of life), was combined with tracheostomy tube use (binary) to form a 6-level categorical variable. This procedure was repeated for g-tube. Main Outcomes: Mean monthly health care costs in last 6 months of life were estimated using a patient-level case-costing algorithm using 2023 CAD$ (CAD$ 1.00 = US$ 0.74) and evaluated by negative binomial regression. Results: The analysis included 11 135 adults who received a diagnosis of HNC from 2007 to 2022 and died before October 1, 2023. They had a mean (SD) age of 68.4 (12.1) years at diagnosis and 8245 were male (74.0%). Nearly 90% received PC: 5866 (52.6%), late PC; 4093 (36.8%), early PC; and 1176 (10.6%) did not receive PC. Regarding tracheostomy/g-tube use in the last year of life, 1293 (11.6%) used a tracheostomy and 1235 (11.1%), a g-tube. Compared to those who did not receive PC nor use a tracheostomy tube, the cost increase on using a tracheostomy tube (rate ratio [RR] 2.93; 95% CI, 2.32-3.71) was higher than using it with early PC (RR, 2.88; 95% CI, 2.63-3.15) but lower than using it with late PC (RR 4.37; 95% CI, 4.00-4.77); results were similar for g-tube use. A large proportion of the cohort had an emergency department visit (9109 [81%]) or a non-PC hospital admission (5419 [48.7%]) in last 6 months of life, with both proportions being the lowest among nonrecipients of PC. Early PC was associated with a 46.8% lower likelihood (odd ratio, 0.53; 95% CI, 0.45-0.63) of experiencing a home death than no PC. Conclusions and Relevance: This cohort study found that receiving a tracheostomy/g-tube in last year of life has pronounced economic implications to the health care system. Early initiation of PC may attenuate this high cost but may not reduce the use of aggressive hospital-based care at the end of life or facilitate home deaths. Team-based early provision of PC for this patient population is required.

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)0.005
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 score0.121
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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