Scheduling Delayed Treatment and Surgeries Post-Pandemic: A Stakeholder Analysis
Bibliographic record
Abstract
Many are interested in how to safely ramp up elective surgeries after national, state, and voluntary shutdowns of operating rooms to minimize the spread of COVID-19 infections to patients and providers. We conducted an analysis of diverse perspectives from stakeholders regarding how to trade off risks and benefits to patients, healthcare providers, and the local community. Our findings indicate that there are a large number of different categories of stakeholders impacted by the post-pandemic decisions to reschedule delayed treatments and surgeries. For a delayed surgery, the primary stakeholders are the surgeon with expertise about the clinical benefits of undergoing an operation and the patient's willingness to tolerate uncertainty and the increased risk of infection. For decisions about how much capacity in the operating rooms and in the inpatient setting after the surgery, the primary considerations are minimizing staff infections, preventing patients from getting COVID-19 during operations and during post-surgical recovery at the hospital, conserving critical resources such as PPE, and meeting the needs of hospital staff for quality of life, such as child care needs and avoiding infecting members of their household. The timing and selection of elective surgery cases has an impact on the ability of hospitals to steward finances, which in turns affects decisions about maintaining employment of staff when operating rooms and inpatient rooms are not being used.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".