Bibliographic record
Abstract
To the Editor: The novel coronavirus disease (COVID-19) pandemic has affected all aspects of the health care system. As the pandemic has evolved, so too have trainees’ clinical responsibilities. Many, setting aside their subspecialty training, were reassigned to different rotations based on systemic need. For many in the procedural specialties, these reassignments raised trepidation over losing valuable opportunities to acquire and hone skillsets. The pandemic has also taken providers out of their comfort zones—whether into operating rooms or into outpatient clinics—to learn to care for patients with this highly communicable and potentially lethal disease. The donning of personal protective equipment has taken on an entirely new meaning for those in surgery, one that has shifted from safeguarding the sterility of an operation to protecting oneself from a virus that can kill. In the beginning, it was easy for us cardiothoracic surgical residents to view this transformation negatively, as a disruption taking us off our chosen paths. However, over time we began to realize that we are changing for the better, perhaps in ways we would never have actualized if it were not for COVID-19. The culture in surgery often defines courage as being independent and projecting confidence. Many of us surgeons prescribe to the notion that stoic, solitary struggles help build strength, and we seldom reach out to others for help. Courage is often viewed as the opposite of vulnerability. Given the worldwide calamity, we have had to adapt our definition of courage to best care for our patients, serve their families, and preserve ourselves. We have learned to demonstrate empathy, to lean into emotionally challenging conversations (often concerning end-of-life care), to acknowledge that we do not have the solutions, and to be vulnerable with each other. Courage is no longer the antithesis of vulnerability; rather, the pandemic has taught us a new definition of courage: courage is the ability to embrace and be at peace with vulnerability. One of the interns on our team texted the group after losing a patient to COVID-19. “I just called the family,” his text read, “The son’s response was, ‘You will always be my hero. Thank you for all that you do.’” He shared the vulnerability and loneliness he felt in that moment. We all replied with words of encouragement. I wonder if, months ago, before the pandemic, we would have been as willing to share our feelings so openly. Acknowledgments: The authors would like to thank their colleagues for their willingness to share their stories.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".