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Record W4407531087 · doi:10.3332/ecancer.2025.1847

Return to work in head and neck cancer survivors: an exploratory multimethod study at a cancer centre in Santiago, Chile

2025· article· en· W4407531087 on OpenAlexaff

Bibliographic record

Venueecancermedicalscience · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineHead and neck cancerCancerExploratory researchGerontologyInternal medicineSocial science

Abstract

fetched live from OpenAlex

Objective: Head and neck cancer (HNC) survivors may suffer from functional and psychosocial impairment, and thus, return to work (RTW) often poses challenges. A paucity of evidence on this subject exists in Chile and the region. The aim of this paper is to describe and characterize the RTW of HNC survivors treated at a cancer centre in Santiago. Methods: This study employed an exploratory, cross-sectional design, with a multimethod, quantitative approach. Surgically treated patients with HNC between 2016 and 2022 were invited to participate. Clinical and sociodemographic data were statistically analysed to establish associations with RTW. Participants were surveyed about their process of RTW and income variation. Results: = 0.046). No association was found between disease status, tumour location or treatment received and RTW. Of those who resumed working, a third had less income. Job accommodations were made on a case-by-case basis. A third of the survivors decreased their workload. Conclusion: Being a woman was associated with less RTW. Future interventions should provide support in reintegration into the workplace. This study constitutes the first published data on RTW in Chilean patients.

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 score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
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 head0.026
GPT teacher head0.363
Teacher spread0.337 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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