Prevention and improved management of serious neurological adverse events during praziquantel-based mass drug administration in a Taenia solium endemic area: Experiences from Madagascar
Bibliographic record
Abstract
Mass drug administration (MDA) programs involving praziquantel are used in public health programs to control diseases such as schistosomiasis, taeniasis caused by Taenia solium, opisthorchiasis and clonorchiasis. Praziquantel is a systemically distributed anthelmintic drug also used to treat neurocysticercosis (NCC) caused by the larval stages of T. solium in the central nervous system. The doses of praziquantel used in MDA are low compared to those used for the treatment of NCC, but in people with latent NCC (without symptoms or signs), there is a potential risk of neurological adverse events (AE) due to the development of inflammation around the cysts following administration. In Madagascar two large MDA campaigns aimed at T. solium were conducted using praziquantel in the Vakinankaratra region. Prior to the first MDA campaign, we implemented a program designed to minimize the occurrence of neurological AE and improve their management, which included training of health agents and community workers as well as health centres staff, population awareness, post-MDA active and passive surveillance and the supply of basic medicines to health centres. This program was repeated for the second MDA campaign. A total of 117,216 and 163,089 people were treated during the first and second MDA campaign respectively, with 10 participants experiencing serious AE, which were successfully managed. The beneficial results from our program in Madagascar can help other programs and countries using MDA with praziquantel in T. solium endemic areas to improve the safety of these campaigns.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".