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Record W4394031394 · doi:10.5281/zenodo.7031181

Data set of Sepsis awareness at the university hospital level: a survey-based cross-sectional study

2022· dataset· en· W4394031394 on OpenAlexaff
Jean, Santino, Tapio, Marie-Annick, Sylvain

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCross-sectional studySet (abstract data type)Data setMedicineComputer scienceArtificial intelligencePathologyProgramming language

Abstract

fetched live from OpenAlex

This is the dataset for the SAfE survey We conducted a survey on nurses and physicians distributed over all adult departments of the Lausanne University Hospital (LUH) and local paramedics. The survey aimed to assess professionals’ demographics (age, profession, seniority, unit of activity), prior sepsis education, perceptions, knowledge of sepsis epidemiology, definition, recognition and management. Correlation between surveyed personel and sepsis perceptions and knowledge were assessed with univariate logistic regression models. Methods: The research team designed a survey inspired from previously published surveys assessing knowledge and awareness of sepsis. The questions were tailored to the profession (clinical scenario adapted activity sector - medicine, surgery, emergency department or gynecology). The survey was written and completed in French. Each section of the survey (paramedics’, nurses’ and physicians’ section) was submitted to three focus groups consisting of 3 to 6 participants of each profession, commonly involved in care of patients with sepsis. These focus groups assessed the applicability and appropriateness (validity) of the survey. The focus group were constituted of nurses, physicians and paramedics of all seniority levels. Their primary task was in assessing whether formulations and relevance of questions were adequate. The survey was revised using feedback from the groups. Surveys of nursing staff and paramedics were more focused on screening, initial evaluation and early management whereas physicians were also tested on diagnosis and management. Responses options included Likert-type scales, binary (e.g. “yes/no”) or multiple choices. Each question was locked upon answering, which prevented post hoc changes that could be influenced by information provided at a later stage of the survey. The final survey contained questions on participants’ demographic characteristics (5/7/6 questions for nurses/paramedics/physicians), sepsis continuous education (3/3/3 questions), self-evaluation of sepsis knowledge and clinical management (2/2/2 questions), definitions, scores and epidemiology (11/12/14 questions), and sepsis management (4/4/5 questions). The survey was developed in REDCap (Research Electronic Data Capture) software so as to automatically export participants’ responses to a database. Surveys are provided as supplementary material (supp. meth. survey). Participants were recruited between January 20 and October 10, 2020. We aimed for a convenience sample size of 1,000 persons (approx. 20% of the active HCPs) distributed over all departments (Emergency department (ED), intensive care unit (ICU), Medicine, Paramedic, Psychiatry, or Surgery) and professions (paramedics, nurses and physicians) to reach 20% of LUH staff considered HCPs, being as representative as possible. Pediatrics and neonatology staff (not covered by Sepsis-3 consensus definitions) as well as nurses and physicians not in daily contact with patients (i.e., who were working in research team or in administration) were excluded. We favored a supervised approach rather than a dissemination of the survey to all HCPs by email. Participants answered the online survey under trained interviewer supervision so as maximize data quality and to avoid biased responses (internet queries, discussions between colleagues). Furthermore, to avoid multiple answers by a same HCP, surveys were accessed by QR-code only available at screening; timing of survey completion was registered and email addresses were registered. Thus, participants were screened amongst the medical (n=1664) and nursing staff (n=2463) in daily contact with patients of LUH and amongst paramedics of the Canton of Vaud (n=290) during the screening period. Screening by trained interviewers took place during scheduled patient hand-offs, seminars or group meetings, as permitted by heads of units. Participation was voluntary and anonymous. Participants completed the online survey using tablets or smartphones (participants’ or provided by the investigators). Results: Between January and October 2020, 1,116 of 1,216 contacted professionals completed the survey (participation rate 91.8%). These participants represented about 25% of the workforce (n=4417) – i.e., 25.1% of nurses (619/2,463), 20.9 % of physicians (348/1,664),and 51.4% of paramedics (149/290). Only 13% of participants (physicians: 28.4%, nurses: 5.9%, paramedics: 6.8%) correctly identified the Sepsis-3 consensus definition. Similarly, less than 50% of participants (physicians: 48.6%, nurses: 10.0%) selected the SOFA score as a sepsis defining score for infected patients. Furthermore, 24% of participants properly identified the qSOFA score as a predictor of increased mortality; and 6% selected correctly its components. For a suspected sepsis, 96.1%, 91.6% and 75.8% of physicians respectively chose blood cultures, broad-spectrum antibiotics and fluid resuscitation as required interventions; 76.4% and 18.2% of physicians requested initial measures within 1 and 3 hours, respectively. For physicians, recent training correlated with awareness regarding definitions, SOFA and qSOFA score use and components: ORs (95%CI) 2.2 (1.4-3.6), 4.3 (2.7-6.7), 3.4 (2.2-5.2), and 2.6 (1.5-4.6), respectively). Conclusions: We identify a deficit of awareness among physicians, nurses and paramedics at LUH correlating with a lack of sepsis-specific training.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.006

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 head0.180
GPT teacher head0.374
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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