213.2: Referral rates and hospitalization characteristics of referred and non-referred patients meeting GIVE clinical triggers prior to enactment of mandatory referral legislation
Notice bibliographique
Résumé
Introduction: Adequate legislation is a key component of high performing deceased donation systems. However, success is also reliant on healthcare provider identification and timely referral of patients meeting clinical triggers. Awareness, attitudes and knowledge may be associated with identification and referral. Non-referral has previously been attributed to patient characteristics such as older age, death from non-neurologic causes, and presence of chronic organ disease; other factors may include hemodynamic instability, palliation without planned WLST, and next-of-kin decline prior to intended referral. In 2021 in NS enacted mandatory referral legislation for patients meeting clinical triggers and intentionally broadened referral criteria to shift responsibility of identifying medically suitable potential donors from bedside clinicians to organ donation specialists. This study aimed to compare the hospital characteristics of patients referred and not referred to the organ donation program prior to these system changes. Method: Retrospective audit of Legacy of Life database for all patient deaths in Nova Scotia Health hospitals between 2017 and 2020 (4 years). Patients meeting clinical triggers for referral (primarily ventilated within 12 hours of death) were included, excluding patients who died during unsuccessful cardiopulmonary resuscitation. We examined annual referral rates, reasons for non-referral, and compared hospitalization characteristics of referred and non-referred patients. Results: Of 1699 patient deaths meeting clinical triggers for referral, 186 (10.9%) were referred. Referral rates increased from 4.8% in 2017 to 16.8% in 2020. The most common reason listed for non-referral (n=1513) was imminent move to comfort care requested by next-of-kin (n=1295; 85.6%). Although there were a similar number of deaths in academic hospitals (n=972; 57.2%) and non-academic hospitals (n=727; 42.8%), a higher proportion of deaths in academic hospitals (n=154; 15.8%) were referred than in non-academic hospitals (n=32; 4.4%). Higher rates of referral were observed in units with a greater number of deaths – Intensive Care Units (176 of 1399; 12.6%), Intermediate Care Units (5 of 135; 3.7%), Emergency Department (5 of 165; 3.0%). Conclusion: We documented a high rate of missed referrals of patients meeting clinical triggers for referral to the organ donation program in Nova Scotia in the 4 years prior to enactment of mandatory referral legislation and intentionally broadening referral criteria to shift responsibility of identifying medically suitable potential donors from bedside clinicians to organ donation specialists. Targeted educational initiatives with particular focus on non-academic centres and areas outside the Intensive Care Unit may be beneficial. Further exploration of reasons for non-referral is required.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».