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Record W4413967091 · doi:10.3332/ecancer.2025.1984

Potentially avoidable emergency department visits among patients with advanced cancer

2025· article· en· W4413967091 on OpenAlexaff
Miguel Araujo-Meléndez, Jacqueline Alcalde-Castro, Andrea de-la-O-Murillo, Thierry Hernández‐Gilsoul, Enrique Soto‐Pérez‐de‐Celis, Roberto Gonzalez-Salazar, Yanin Chávarri-Guerra

Bibliographic record

Venueecancermedicalscience · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineEmergency departmentMedical emergencyCancerEmergency medicineFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background: Potentially avoidable emergency department (ED) visits are considered an indicator of the quality of cancer care.Objective: To investigate the causes of ED visits of patients with advanced cancer. Methods:We included in this analysis the visits to the ED of patients with advanced cancer in a tertiary cancer center in Mexico City, registered the reasons for their visit, and classified them as potentially avoidable or not by three independent observers. Results:Seventy-seven patients were included, and 69% had at least one visit to the ED.Fifty-seven percent of visits were classified as potentially avoidable.The most common causes of visiting the ED were: pain, gastrointestinal disorders and ascites.Patients with gastrointestinal and genitourinary tumours had a higher frequency of unavoidable ED visits compared to patients with other tumours (43.3% versus 20.7%, p 0.03). Conclusion:A significant proportion of patients with advanced cancer visit the ED and many of these visits were classified as potentially avoidable based on expert judgment and adapted criteria.These findings highlight the need for further research and contextspecific strategies, such as care and early palliative integration, to safely reduce unnecessary ED use and enhance quality of life in low-and middle-income settings.

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 score0.038
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.371
Teacher spread0.350 · 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
Published2025
Admission routes1
Has abstractyes

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