Delivering modern, high-quality, affordable pathology and laboratory medicine to low-income and middle-income countries: a call to action
Bibliographic record
Abstract
[Comment on 13.5, 13.6 & 13.7] Published just a week before the World Health Organization put out their first Essential Diagnostics List (http://www.who.int/medical_devices/diagnostics/WHO_EDL_2018.pdf), this series of 3 papers highlights the previously often unrecognized but important role of pathology and laboratory medicine (PALM) services in low- and middle-income countries (LMIC). Increasingly, modern medicine relies on diagnostic testing to confirm clinical diagnoses, including through in vitro diagnostics, devices, pathology and radiology procedures – often avoiding morbidity, mortality and a negative economic impact from a wrong diagnosis. Endocrine conditions such as diabetes and lipid metabolism disorders are prime examples that can be difficult to diagnose, classify, treat and monitor based on clinical grounds, especially in their asymptomatic stages. While in high-income countries PALM are used for 2 out of 3 health conditions, LMICs continue to have limited access to PALM services despite bearing a disproportionate share of the global burden of disease with much scarcer access to resources.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.066 | 0.066 |
| Insufficient payload (model declined to judge) | 0.034 | 0.030 |
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".