Bibliographic record
Abstract
The article by Bolton et al1 published this week’s journal highlights an important and often overlooked issue in surgical research—the need for a usable approach to evaluation of surgical and technological innovations in low-income and middle-income countries (LMICs). Bolton et al propose a route toward major improvements in the ability of LMIC surgeons to evaluate their own practices. As the Lancet Commission on Global Surgery showed, the gap between capacity and population surgical needs in most of the world is staggering.2 An estimated 70% of humanity is effectively unable to access even life-saving surgery,3 4 due to lack of affordability, infrastructure and workforce. In Africa, there are only 0.7 specialist surgeons, obstetricians and anesthesia providers per 100 000 capita, far short of the recommended surgical workforce density of 20–40/100,000.5 Access to training and basic equipment in many settings is extremely limited, and even basic infrastructure such as electricity and water is not guaranteed. At the same time, many LMIC surgeons receive sophisticated equipment as aid or donations, which often ends up in a ‘donations graveyard’ due to lack of interoperability, maintenance, infrastructure, training and related supplies. Surgical research is almost absent in many LMICs, yet LMIC surgeons are innovative by necessity, frequently developing ‘frugal’ adaptations which allow them to do more with less.6
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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 | 0.050 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 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".