The Western Cape Surgical Recovery Project: experience at Groote Schuur Hospital
Bibliographic record
Abstract
Background Data from six Western Cape secondary-level hospitals have shown that during the first wave of the COVID-19 pandemic (which lasted from May to July of 2020), total surgeries decreased by 44%, and elective surgeries by 74%, due to secondment of nursing, anaesthetic and surgical staff to COVID high-care and intensive-care services. At Groote Schuur Hospital, the loss of surgical output over the two years of the pandemic-related surgical service de-escalation (2020-2021) was estimated at 10 000 cases, with 6 000 patients with progressive disease waiting for elective surgical care. Methods In early May 2022, a Surgical Recovery Project was initiated; funding from the Western Cape Department of Health, and donations from the Gift of the Givers Foundation, private individuals, businesses, and other non-governmental organisations were used to build, staff, and equip a Day-Case Surgery Suite. Results By the Project midway point (end October 2022), a total of 800 extra cases had been completed, and the Project is currently on track to exceed the target of 1 500 cases in a calendar year by at least 10%. The largest number of procedures done were eye cases (n = 191), followed by cases involving surgery to the integumentary system (n = 141), and musculoskeletal system cases (n = 123). There were a total of 30 patient cancellations. While the Project expectedly had poorer on-time-start statistics in the first quarter of operation (range 0.0 - 6.9%), the percentage of on-time-start statistics improved markedly over the second quarter (range 43.3 - 56.5%). World Health Organization checklists were completed for 85.1% of operations performed at the Day-Case Surgery Suite, and no adverse incidents or mortalities were recorded at the Unit. Conclusions This project demonstrates that the volume of services provided in the public sector can be escalated with the use of external funding of capital for human resources, equipment and consumables. However, these services become truly effective when there is sufficient multi-disciplinary planning, alignment and support, at operational, strategic and executive levels of healthcare facilities.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".