New global surgical and anaesthesia indicators in the World Development Indicators dataset
Bibliographic record
Abstract
Although 5 billion people lack access to surgery and anaesthesia care, little systems-level data exist to address this health inequity and social injustice.1 Data drive quality improvement processes in business and health systems in high-resource settings, but clinicians and policymakers in low-resource environments have been metaphorically—and often literally—operating in the dark. The challenges to obtaining accurate health systems data involve nearly all clinical delivery platforms in global health and have been well documented and are also relevant to surgery and anaesthesia.2 They include insufficient national-level investment in analytics, insufficient donor investment in data collection, little analysis of global health funding streams, limited tools and resources for data collection at the local level, and limited accessibility of collected data to those best positioned to implement data-driven solutions. Such gaps undermine advocacy, as the problems remain invisible and thus fail to inspire political will.In January 2014, at the inception of a global surgical movement designed to realign stakeholders into a structured approach to surgical systems strengthening, Dr Jim Kim, President of the World Bank Group, challenged The Lancet Commission on Global Surgery (LCoGS) to develop consensus-based indicators and time-bound targets to track progress. Sixteen months later, in April 2015, after thorough consultation with clinicians, researchers, hospital administrators and policymakers, the Commission recommended six core indicators to assess surgical and anaesthesia systems strength.3 When these indicators (summarised in table 1) are considered together, they serve as basic proxies of surgical health system functioning.View this table:In this windowIn a new windowTable 1 Data obtained per indicator including totalThe LCoGS indicators assess multiple aspects of surgical and anaesthesia care delivery within a country. Where are the facilities capable of providing surgical care and how close are they to the populations that need them? How many surgical and anaesthesia providers are present? What quantity of surgical care is provided to a population? …
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".