Shared learning in and beyond the COVID-19 pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.036 | 0.037 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".