An insight into the Success, Challenges, and Future perspectives of eliminating Neglected tropical disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neglected Tropical Diseases (NTDs) are debilitating, chronic illnesses that have the power to prolong poverty due to their negative effects on productivity, child development, social embarrassment, and maternal problems. Insufficient resources are available to raise awareness of these illnesses or locate previously unknown cases. More than one billion people worldwide are still affected by NTDs, which have significant social and financial repercussions for developing nations. Global targets have been set by the World Health Organization to prevent, control, eliminate, or eradicate NTDs, as well as broad and interdisciplinary aims linked to the Sustainable Development Goals targets. The Kigali Declaration, in which leading organizations and countries declared fresh commitments to step up efforts to eradicate NTDs, and the London Declaration aimed to control and eradicate ten NTDs by 2020. We reviewed the epidemiology and burden of NTDs, the success and challenges of eliminating NTDs, as well as therapeutic interventions for the management of NTDs. We also opined on future directions necessary for effective and holistic eradication of NTDs in affected regions. The successful eradication of NTD will significantly increase the socioeconomic and educational levels of the affected countries, thereby increasing the productive workforce and assisting in the accomplishment of some sustainable development goals. As a result, there is a need for global commitment to funding drug research and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it