Twice neglected? Neglected diseases in neglected populations
Bibliographic record
Abstract
It is unfortunately true that clinicians lack the necessary evidence to know how to use medications properly in large sections of the population and do not have optimal treatments to use for many neglected tropical diseases (NTDs). NTDs often disproportionately affect neglected populations that are left out of research efforts, such as children and pregnant women. As reliable access to safe, effective preventives and treatments can break the cycle of poverty, illness, and ensuing debility that further perpetuates poverty, it is of paramount importance to investigate and develop new medicines for neglected populations suffering from NTDs. Furthermore, there is not only a need to develop and evaluate novel therapies, but also to ensure that these are affordable, available, and adapted to the communities who need them. The NIH has proposed a "4 C's" framework which is relevant for neglected diseases and populations and should be leveraged for the study of the Twice Neglected: Consider inclusion; Collect data from neglected populations with neglected conditions; Characterize differences through meaningful analysis; Communicate findings pertaining to neglected diseases and populations. With this editorial, the British Journal of Clinical Pharmacology hereby launches a call for high-quality articles focusing on NTDs in special populations, to facilitate and encourage the reversal of this dual neglect.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".