Shared care in surgery: Practical considerations for surgical leaders
Bibliographic record
Abstract
The recent COVID-19 pandemic has highlighted limitations in current healthcare systems and needed strategies to increase surgical access. This article presents a team-based integration model that embraces intra-disciplinary collaboration in shared clinical care, professional development, and administrative processes to address this surge in demand for surgical care. Implementing this model will require communicating the rationale for and benefits of shared care, while shifting patient trust to a team of providers. For the individual surgeon, advantages of clinical integration through shared care include decreased burnout and professional isolation, and more efficient transitions into and out of practice. Advantages to the system include greater surgeon availability, streamlined disease site wait lists, and promotion of system efficiency through a centralized distribution of clinical resources. We present a framework to stimulate national dialogue around shared care that will ultimately help overcome system bottlenecks for surgical patients and provide support for health professionals.
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 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 | 0.143 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.036 |
| Scholarly communication | 0.029 | 0.045 |
| Open science | 0.009 | 0.057 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".