Gaps and priorities in innovation for children's surgery
Bibliographic record
Abstract
Lack of access to pediatric medical devices and innovative technology contributes to global disparities in children's surgical care. There are currently many barriers that prevent access to these technologies in low- and middle-income countries (LMICs). Technologies that were designed for the needs of high-income countries (HICs) may not fit the resources available in LMICs. Likewise, obtaining these devices are costly and require supply chain infrastructure. Once these technologies have reached the LMIC, there are many issues with sustainability and maintenance of the devices. Ideally, devices would be created for the needs and resources of LMICs, but there are many obstacles to innovation that are imposed by institutions in both HICs and LMICs. Fortunately, there is a growing interest for development of this space, and there are many examples of current technologies that are paving the way for future innovations. Innovations in simulation-based training with incorporated learner self-assessment are needed to fast-track skills acquisition for both specialist trainees and non-specialist children's surgery providers, to scale up access for the larger population of children. Pediatric laparoscopy and imaging are some of the innovations that could make a major impact in children's surgery worldwide.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".