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Record W4413101920 · doi:10.4103/cjrm.cjrm_60_24

Sepsis management in a rural hospital in Northwest Ontario

2025· article· en· W4413101920 on OpenAlexaffvenueabout
Len Kelly, Sharen Madden, Catherine Wong, Sonya Fewer, Tamar Shemesh-Lobl, Shanthive Asokan

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSchwartz/Reisman Emergency Medicine InstituteUniversity of TorontoNOSM University
Fundersnot available
KeywordsSepsisMedicineIntensive care medicineGeographyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: A retrospective quantitative study of emergency department (ED) management of patients diagnosed with sepsis in a rural hospital in northwest Ontario was conducted to understand the management of sepsis guidelines and department performance. METHODS: Our study includes all adult ED patients (18+) receiving an ED diagnosis of sepsis as identified by hospital medical records coding according to National Ambulatory Care Reporting System diagnostic codes from January 1, 2021 to December 31, 2021. Charts were reviewed for patient demographics, initial presentation, timing of ED management and outcomes. Statistical analysis involved descriptive methods. RESULTS: Twenty-five adults were eligible, 16 female, average age 53.1 years (standard deviation [SD] = 21.9). Mortality rate was 28%. The average age of the 7 non-survivors was 64 (SD = 14.73). Time to nursing assessment averaged 8 min (SD = 7.27); time to MD assessment was 41 min (SD = 30.93). Canadian Triage and Acuity Scale scores were urgent or higher in 20 of 23 cases. Retrospective Systemic inflammatory Response Syndrome scores were elevated in 17/25 cases. Average time to fluids administration was 85 min (SD = 79.41). Sixteen of the 23 patients with recorded times of antibiotic delivery received them in 180 min or less. CONCLUSION: Patients with sepsis present with a variety of clinical features, and laboratory findings. Awareness of the urgency of sepsis treatment and an organised departmental approach is important in the management of potentially septic patients. INTRODUCTION: Une étude quantitative rétrospective sur la prise en charge aux urgences des patients diagnostiqués avec une septicémie dans un hôpital rural du nord-ouest de l'Ontario a été menée afin de comprendre les directives en matière de prise en charge de la septicémie et les performances du service. MTHODES: Notre étude porte sur tous les patients adultes (âgés de 18 ans et plus) admis aux urgences et ayant reçu un diagnostic de septicémie selon le codage des dossiers médicaux de l'hôpital, conformément aux codes diagnostiques du Système national d'information sur les soins ambulatoires (SNISA), entre le 1er janvier 2021 et le 31 décembre 2021. Les dossiers ont été examinés afin de recueillir des données démographiques sur les patients, les premiers symptômes, le moment de la prise en charge aux urgences et les résultats. L'analyse statistique a été réalisée à l'aide de méthodes descriptives. RSULTATS: Vingt-cinq adultes étaient admissibles, dont 16 femmes, avec un âge moyen de 53.1 ans (écart-type = 21,9). Le taux de mortalité était de 28%. L'âge moyen des seven patients décédés était de 64 ans (écart-type = 14,73). Le délai moyen avant l'évaluation infirmière était de 8 min (écart-type = 7,27); le délai avant l'évaluation médicale était de 41 min (écart-type = 30,9). Les scores de l'échelle canadienne de triage et d'acuité étaient urgents ou supérieurs dans 20 cas sur 23. Les scores rétrospectifs du syndrome de réponse inflammatoire systémique étaient élevés dans 17 cas sur 25. Le délai moyen avant l'administration de liquides était de 85 min (écart-type = 79,41). Seize des 23 patients pour lesquels le délai d'administration des antibiotiques a été enregistré les ont reçus en 180 min ou moins. CONCLUSION: Les patients atteints de septicémie présentent divers signes cliniques et résultats de laboratoire. Il est important de connaître l'urgence du traitement de la septicémie et d'adopter une approche organisée au sein du service pour prendre en charge les patients potentiellement septiques.

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 score0.436
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.285
Teacher spread0.265 · 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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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