Global surgery research collaborations during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic created an unprecedented burden on health systems, including surgical services, which have been indirectly affected by the growing number of cases due to cancellation of operations, delayed screening and a lack of adequate resources such as PPE and ventilators. In addition to logistical challenges, the pandemic also raised imminent clinical questions that required immediate answers. Global collaborations have been vital to identifying challenges by pooling data and collecting evidence to provide critical information to guide clinical and surgical care. Research partnerships have been the driving force behind global surgery research; however, since the pandemic, there has been an increased need for equitable collaboration and innovation between high-income and low-income research institutions to continue making steady progress towards providing access to safe, affordable surgical care. This article explores academic research partnerships formed during the pandemic and identifies challenges and opportunities presented to researchers and institutions. Finally, this paper recommends that further collaborations be made between HIC and LMICs to ensure policies that global surgery ensures that key stakeholders are at the centre of research. Such policies need to focus on the access to education and mentorship, micro-grants for researchers, and publication opportunities.
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 | 0.090 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".