Shared learning in and beyond the COVID-19 pandemic
Notice bibliographique
Résumé
The COVID-19 pandemic has cost the lives of over 1.5 million people to date and resulted in severe surgical backlogs up to tens of millions of surgeries worldwide [1]. Steinmaurer and Bley [2] appropriately question whether the transformability of cardiac surgery in high-income country epicentres of the COVID-19 pandemic can lead to changes elsewhere in the world. Six billion people lack access to safe, timely and affordable cardiac surgical care when needed, and this pandemic has only aggravated disparities in access to care [3, 4]. As countries have adapted and vaccines are on the horizon, it is paramount to think above and beyond what we have learned in our specialty during these challenging times and recognize the sustained disparities across the globe. These disparities can be further explored by assessing service provision and workforce capacity in low- and middle-income countries (LMICs). This is especially prominent in low-income countries, where 0.04 cardiac surgeons are available per million population compared to 7.15 in high-income countries [4]. The loss of even 1 surgeon can lead to disastrous consequences in service provision. Now, travel restrictions imposed due to the pandemic have substantially increased these discrepancies. LMIC centres acting as regional hubs, often offering free or subsidized surgery, have experienced significant volume reductions while adapting to COVID-19 responses [4]. The pandemic also affected visiting teams, who have been unable to reach regions where local capacity is scant. These issues signpost the need for urgent solutions. The pandemic has emphasized the importance of a global health view for cardiac surgery. Mutual learning can act as a vector for exponential change and improvement in meeting these disparities. George et al. [5] have described multiple strategies used in the New-York Presbyterian Hospital within their cardiac surgical service such as split ventilation and using additional operating room space for intensive care beds. Such innovations may be utilized to increase the long-term cardiac surgical capacity in LMICs in intensive care units, which can be rate-limiting factors when deciding to take on new patients. In addition, personal protective equipment may be preserved by reducing the number of personnel scrubbed in and switching between operations [5]. This was mirrored in Boston Children’s Hospital, where do-it-yourself elastomeric respirators were developed as a result of N95 shortages [6]. With such low-cost options being successfully incorporated into high-performance units, these examples highlight the importance of shared learning and its symbiotic relationship. The COVID-19 era has facilitated change in clinical practice to reach a new normal, but with recent developments of imminent vaccine rollout, there is hope for resolving the challenges presented to us both in the short and long terms. With high-income countries dictating and dominating vaccine distribution, we can expect a significant hiatus before adequate herd immunity can be established in LMICs. As a result of these economic imbalances, cardiovascular care disparities will continue to pose a substantial burden. It is our moral responsibility to recognize the privileged position we inhabit and use the experiences from this pandemic to fuel shared learning and bilateral partnerships. Conflict of interest: none declared.
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 | 0,007 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,006 | 0,013 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,036 | 0,037 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,007 |
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 ».