Essential Diagnostics: A Key Element of Universal Health Coverage
Bibliographic record
Abstract
Good primary care is an essential precondition for a decent healthcare system. In fact, primary health care is at the heart of Universal Health Coverage (UHC). UHC, in turn, is critical to achieve the sustainable development goals. While access to essential medicines is explicit in UHC, access to essential diagnostics has received little attention. In May 2018, the World Health Organization (WHO) published the first Essential Diagnostics List (EDL), and declared its commitment to give equal importance to diagnostic tests and essential medicines. The EDL has been positively received by a variety of stakeholders, including industry. The EDL offers countries a benchmark that they can use to measure and improve diagnostic services, and preliminary data from India show limited access to essential tests at the primary care level. Some countries, notably India, have already begun developing National EDLs (NEDLs). Hopefully, such national efforts will enable the implementation of EDLs, and improve access to diagnostics. It is time for Low- and Middle-Income Countries (LMICs) to not only increase coverage of health care, but also improve quality of care. Access to essential tests is the first key step in improving quality of care.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".