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Record W4409603547 · doi:10.1186/s12873-025-01191-2

Triage decisions and health outcomes among oncology patients: a comparative study of medical and surgical cancer cases in emergency departments

2025· article· en· W4409603547 on OpenAlexaboutno aff
Anas Alsharawneh, Rami A. Elshatarat, Ghaida Shujayyi Alsulami, Mahmoud H. Alrabab’a, Majed S Al-Za'areer, Bandar Naffaa Alhumaidi, Wesam Taher Almagharbeh, Tahany Fareed Al niarat, Khaled Mohammed Al‐Sayaghi, Zyad T. Saleh

Bibliographic record

VenueBMC Emergency Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriageMEDLINEMedical emergencyCancerEmergency medicineFamily medicineIntensive care medicineGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer-related emergencies are a significant challenge for healthcare systems globally, including Jordan. Effective triage is critical in ensuring timely and accurate prioritization of care, especially for surgical cancer patients requiring urgent intervention. However, under-triage-misclassification of high-acuity patients into lower urgency categories-can lead to significant delays and worsened outcomes. Despite the recognized importance of accurate triage, limited research has evaluated its impact on cancer patients in Jordan, particularly those requiring surgical care. OBJECTIVES: This study aimed to evaluate the timeliness and prioritization of care for cancer patients admitted through the emergency department (ED) in Jordan. The specific objectives were to examine the association between under-triage and treatment delays and assess its impact on key outcomes, including time to physician assessment, time to treatment, and hospital length of stay. METHODS: A retrospective cohort design was used to analyze data from 481 cancer patients admitted through the ED in four governmental hospitals across Jordan. Two cohorts were established: surgical cancer patients requiring emergency interventions and non-surgical cancer patients presenting with other oncological emergencies. Triage accuracy was assessed using the Canadian Triage and Acuity Scale (CTAS), and under-triage was identified when patients requiring high urgency care (CTAS I-III) were misclassified into lower urgency categories (CTAS IV-V). Data were collected from electronic health records and analyzed using multiple linear regression to evaluate the association between under-triage and treatment outcomes. RESULTS: The majority of patients were elderly, with a mean age of 62.6 years (± 10.7), and a significant proportion presented with advanced-stage cancer (83.4% in stages III and IV). Surgical patients frequently exhibited severe symptoms such as acute pain (51.6%) and respiratory discomfort (41.1%). Under-triage rates were 44.1% for surgical patients and 39.4% for non-surgical patients. Among surgical patients, under-triage significantly delayed time to physician assessment (β = 34.9 min, p < 0.001) and time to treatment (β = 68.0 min, p < 0.001). For non-surgical patients, under-triage delays were even greater, with prolonged physician assessment times (β = 48.6 min, p < 0.001) and ED length of stay (β = 7.3 h, p < 0.001). Both cohorts experienced significant increases in hospital length of stay (surgical: β = 3.2 days, p = 0.008; non-surgical: β = 3.2 days, p < 0.001). CONCLUSION: Under-triage in Jordanian EDs is strongly associated with significant delays in care for both surgical and non-surgical cancer patients, highlighting systemic gaps in acuity recognition and triage processes. These findings underscore the need for targeted interventions to improve triage accuracy, particularly through oncology-specific training and the integration of evidence-based tools like SIRS criteria. Enhancing ED processes for cancer patients is crucial to reducing delays, optimizing resource allocation, and improving clinical outcomes in this vulnerable population. CLINICAL TRIAL NUMBER: Not applicable.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.196
GPT teacher head0.515
Teacher spread0.319 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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