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Enregistrement 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 sur OpenAlexaff
Jean, Santino, Tapio, Marie-Annick, Sylvain

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Langueen
DomaineMedicine
ThématiqueClinical Reasoning and Diagnostic Skills
Établissements canadiensUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésCross-sectional studySet (abstract data type)Data setMedicineComputer scienceArtificial intelligencePathologyProgramming language

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,019
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,007
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,180
Tête enseignante GPT0,374
Écart entre enseignants0,193 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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