Pandemic health consequences: Grasping the long COVID tail
Notice bibliographique
Résumé
Emerging evidence suggests that approximately 10% of people who survive Coronavirus Disease 2019 (COVIDAU : PleasenotethatCOVID À 19hasbeendefinedasCoronavirusDisease2019atitsfirst-19) will have lingering symptoms that negatively affect their quality of life, ability to work, and function [1,2].This important group of people with the post-COVID-19 condition may seem small in comparison to the overall number of people with COVID-19 infection [3].However, many patients who survive COVID-19 are likely to have considerable symptom burden, high resource utilization and health service needs, reduced economic productivity, and possibly a shortened life expectancy.The study by Bhaskaran and colleagues published in PLOS Medicine addresses an evolving, poorly studied, and important area of health policy and planning related to the care of patients who survive hospitalization for COVID-19 [4].At face value, the scope of the COVID-19 pandemic is enormous.Within 2 years, nearly 300 million people have been infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARSAU : AU : PleasenotethatSARS À CoV À 2hasbeendefinedasSevereAcuteRespiratorySyndro -CoV-2) virus, and more than 5 million people have died from it [5].But, there is also a long tail to this statistical distribution of hardship.Studies report that numerous patients will continue to experience fatigue, shortness of breath, pain, sleep disturbances, anxiety, and depression [6].More serious organ dysfunction such as pulmonary fibrosis, cognitive impairment, myocarditis, and renal failure may also develop [6].Whether these translate into clinical diagnoses of chronic diseases like interstitial lung disease, dementia, heart failure, and chronic kidney disease remains to be seen.Collectively, the prospect for immense suffering among these individuals will undoubtedly have huge and enduring impacts on healthcare systems globally.As the world continues its largest vaccination effort in history and looks to eliminate the impacts of acute COVID-19, we must not forget that a meaningful minority who survive will transition from an acute to chronic disease state.In turn, management strategies and health resource planning must also appropriately transition.As a multisystem disease, the post-COVID-19 condition will require the involvement of multidisciplinary care teams [7]: Who will help to look after these patients?Bhaskaran and colleagues studied over 164,000 hospitalized adults with COVID-19 matched to an "active control" group of adults hospitalized with influenza and to general population controls.They compared the medium-and long-term risks of hospital admission and death across the 3 study groups.The main findings were that people discharged following hospitalization for COVID-19 had a 2-fold higher associated risk for rehospitalization and death than the general population and similar risks compared to those hospitalized for influenza.These outcomes were most pronounced in the first 30 days following discharge yet remained substantially elevated over time.Further, those hospitalized with COVID-19 were more likely
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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,007 | 0,012 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,006 |
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.
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 ».