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Record W4393381603 · doi:10.1002/hed.27753

Healthcare Utilization of Oral and Oropharyngeal Cancer Patients in Emergency Department and Outpatient Settings: <scp>An</scp> 8‐year Population‐Based Study

2024· article· en· W4393381603 on OpenAlexafffundabout
Masoud MiriMoghaddam, Babak Bohlouli, Hollis Lai, Seema Ganatra, Maryam Amin

Bibliographic record

VenueHead & Neck · 2024
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMedicineEmergency departmentCancerHealth careEmergency medicineDisadvantagedPopulationRetrospective cohort studyOutpatient clinicAmbulatory careFamily medicineEnvironmental healthInternal medicineNursing

Abstract

fetched live from OpenAlex

INTRODUCTION: This study aimed to determine trends in the healthcare utilization by Oral Cavity and Oropharyngeal cancer patients across emergency department (ED) and outpatient settings in Alberta and examine the predictors of ED visits. METHODS: This is a retrospective, population-based, cohort study using administrative data collected by all healthcare facilities between 2010 and 2019 in Alberta, Canada. Trend of visits to different facilities, patients' primary diagnosis, and predictors of ED visits were analyzed. RESULTS: In total, 34% of patients had at least one cancer-related ED visit. With a rise of 31% in cancer incidence, there was a notable upswing in visits to outpatient clinics and community offices, while ED visits decreased. Cancer stage, rural residence, high material deprivation score, and treatments were found as predictors of ED visits. CONCLUSION: Improved symptom management and better care access for disadvantaged and rural oral cancer patients may decrease avoidable ED visits.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.364
Teacher spread0.320 · 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.

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

Citations0
Published2024
Admission routes3
Has abstractyes

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