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Enregistrement W3099780619 · doi:10.2106/jbjs.20.01894

What’s Important: Reopening Lessons from the Big Leagues’ Experiences with COVID-19

2020· article· en· W3099780619 sur OpenAlexaboutno aff
Robby Sikka, Andrew E. Lincoln, Blythe Adamson, Harlan M. Krumholz

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 detection and testing
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBasketballLeagueIsolation (microbiology)Coronavirus disease 2019 (COVID-19)Test (biology)Ice hockeyAdvertisingPublic relationsPsychologyBusinessPolitical scienceMarketingMedicineGeographyPhysical medicine and rehabilitation

Résumé

récupéré en direct d'OpenAlex

As Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), and the Women’s National Basketball Association (WNBA) bring their seasons to a close1, there remains no consensus on the perfect way to play sports during a pandemic. Lessons from the resumption of major professional sports in the United States and Canada may guide other organizations in what steps to take and what missteps to avoid. All of the leagues had abundant resources and structure to control their settings with regard to close contact, activity, and travel. Basketball and Hockey: Isolation and Testing Cadence The NBA players’ strict adherence to the isolation of a controlled campus and the implementation of frequent testing were essential to achieve a 0% positivity rate among players and staff. It was a more difficult effort to create a “bubble” than to maintain it once it existed. Moreover, it may not be the sensitivity of the test that matters the most in reducing the spread, but perhaps the frequency of retesting cadence2. The NBA, the WNBA, and the NHL have shown how detailed protocols and testing mandates can keep groups safe and enable them to function at a high level when complemented by contact tracing and the isolation of positive cases from the start. The logistical challenges of operating a bubble of this size also provide lessons for how smaller bubbles can be established in local communities for sports or other types of gatherings. Soccer: Reporting Time, Protocol Enforcement, and Mask Spotters We have learned that the solutions that were implemented by Major League Soccer (MLS) to decrease the impact of delayed test results were critical and that testing immediately prior to team travel without receiving test results created unacceptable risk. By increasing testing frequency, decreasing the time to results, and adopting innovative solutions, MLS successfully negotiated the shortened season that had been created by the pandemic. The decision to remove teams from tournament play in Orlando, Florida3, and to isolate players who tested positive helped allow MLS to complete its tournament, which required swift and confident decision-making. MLS, like the NBA, emphasized proactive mask education, utilized an anonymous reporting line for individuals to report a violation of medical protocols and other concerns, and enforced strict mask compliance during all indoor activities. This approach emphasized the importance of shared efforts in reducing community spread. This had the added effect of the community taking preventive efforts, which were based on the guidance of scientific and medical leadership, more seriously. Baseball: Travel From baseball, we have expanded our understanding of how to approach travel, specifically, understanding the risks of traveling to and from areas with a high prevalence of coronavirus disease 2019 (COVID-19) cases. There are asymmetries in the prevalence of COVID-19 across the country, and it is no surprise that the teams that were impacted earliest were from cities that had the highest transmission rates of the virus. Sports teams reflected society, and MLB rapidly worked to optimize its testing and masking policies, employing surgical mask mandates and requiring KN95 masks during travel and for at-risk individuals. No strategy is perfect, but one that does not evolve fast enough places any organization at risk. MLB should be lauded for rapidly adopting its approach when it was clear that there was transmission of the virus early in its season and for successfully completing its season outside of a bubble environment. Football: Scheduling, Screening, and High-Risk Populations From football, we are learning how to handle flexibility in scheduling amid positive COVID-19 cases that have been spread within a team (even with daily testing) and unexplained positive test results, and to best protect athletes who have a high body mass index. While laboratory mistakes can happen, false-positive results, which have been experienced by every league, are a reminder for organizations to remain flexible and consider a test’s accuracy, availability, cost, and required resources and time to results when choosing the best test type for their specific screening or diagnostic purposes. Additionally, the majority of National Football League (NFL) linemen are technically “obese” in terms of body mass index, a characteristic that is associated with COVID-19-positive individuals being 113% more likely to be hospitalized than people of healthy weight, 74% more likely to be admitted to an intensive care unit, and 48% more likely to die, according to a recent meta-analysis that was published in Obesity Reviews4. The NFL can provide valuable data on the short and long-term health implications of COVID-19 in these individuals. Testing: Access, Cost, and Applicability Outside of Sports Based on the aforementioned experiences demonstrating the importance of accurate and prompt testing, it seems imperative that these testing practices expand nationally in an affordable fashion moving forward. The NBA and the National Basketball Players Association have supported an effort at Yale University to develop a screening test through its Surveillance with Improved Screening and Health (SWISH) Study, which has yielded the first U.S. Food and Drug Administration (FDA) Emergency Use Authorization for a saliva-screening protocol5,6. This method of testing was designed to avoid supply-chain bottlenecks, uses less personal protective equipment (PPE), and is cheaper because it relies on “ingredients” that are readily available to laboratories or can be inexpensively purchased. The generic open-source testing protocol, essentially a recipe that any Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory can implement with a license from Yale, is a public good that should drive down the price of testing and increase the supply of COVID-19 tests, and is even safer for health-care workers. As of October 2020, it is being shared with dozens of laboratories and dozens of school districts across the country to begin testing, for zero profit, and is being implemented in school districts across multiple states. It is time to make safe reopening happen, and professional sports teams are helping to lead the way. We need to share what we learn each and every day from professional and collegiate sports teams in order for society to benefit.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,416
Score d'incertitude au seuil0,250

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,155
Tête enseignante GPT0,321
Écart entre enseignants0,165 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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