Life or limb: an international qualitative study on decision making in sarcoma surgery during the COVID-19 pandemic
Bibliographic record
Abstract
Objectives The COVID-19 pandemic is unprecedented as a global crisis over the last century. How do specialist surgeons make decisions about patient care in these unprecedent times? Design Between April and May 2020, we conducted an international qualitative study. Sarcoma surgeons from diverse global settings participated in 60 min interviews exploring surgical decision making during COVID-19. Interview data were analysed using an inductive thematic analysis approach. Setting Participants represented public and private hospitals in 14 countries, in different phases of the first wave of the pandemic: Australia, Argentina, Canada, India, Italy, Japan, Nigeria, Singapore, Spain, South Africa, Switzerland, Turkey, UK and USA. Participants From 22 invited sarcoma surgeons, 18 surgeons participated. Participants had an average of 19 years experience as a sarcoma surgeon. Results 17/18 participants described a decision they had made about patient care since the start of the pandemic that was unique to them, that is, without precedence. Common to ‘unique’ decisions about patient care was uncertainty about what was going on and what would happen in the future (theme 1: the context of uncertainty), the impact of the pandemic on resources or threat of the pandemic to overwhelm resources (theme 2: limited resources), perceived increased risk to self (theme 3: duty of care) and least-worst decision making, in which none of the options were perceived as ideal and participants settled on the least-worst option at that point in time (theme 4: least-worst decision making). Conclusions In the context of rapidly changing standards of justice and beneficence in patient care, traditional decision-making frameworks may no longer apply. Based on the experiences of surgeons in this study, we describe a framework of least-worst decision making. This framework gives rise to actionable strategies that can support decision making in sarcoma and other specialised fields of surgery, both during the current crisis and beyond.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".