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Record W3128859392 · doi:10.1093/ejcts/ezab023

Shared learning in and beyond the COVID-19 pandemic

2021· letter· en· W3128859392 on OpenAlexaff
R. Ravishankar, Najah Adreak, Dominique Vervoort

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2021
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMedicineAortic dissectionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakThoracic aortic aneurysmDissection (medical)Mortality rateAneurysmSurgeryAortic aneurysmRadiologyInternal medicineVirologyAortaDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.036
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0040.006
Scholarly communication0.0060.013
Open science0.0020.007
Research integrity0.0360.037
Insufficient payload (model declined to judge)0.0130.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.143
GPT teacher head0.377
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2021
Admission routes1
Has abstractyes

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