The Benzathine Penicillin G (BPG) Reformulation Preferences Study - Samoa
Bibliographic record
Abstract
Abstract Acute Rheumatic Fever (ARF) is an autoimmune condition caused by untreated group A streptococcal (GAS) infection of the upper respiratory tract (and possibly skin). Multiple or severe attacks of ARF can cause cardiac damage known as rheumatic heart disease (RHD). In Australia, New Zealand (NZ) and the Pacific Region, the disease burden of ARF and RHD amongst Indigenous and Pacific communities is one of the highest in the world, usually affecting children and young adults. The most effective recommended management for ARF requires monthly intramuscular injections of 1.2 million units of Benzathine Penicillin G (BPG) known as secondary prophylaxis (SP) for 10 years or more. The goal of SP is to prevent GAS infections that may lead to the recurrence of ARF. Even with these monthly BPG injections, adherence to SP schedules are usually low due to the frequency and duration of injection, pain and access to proper and timely healthcare. A less painful and longer acting BPG formulation would ideally help prevent recurrence of ARF and improve compliance rates to this schedule with improved understanding of barriers and novel approaches to BPG delivery urgently needed. To better understand the BPG reformulation preferences of children/teens currently receiving monthly BPG intramuscular injections, and that of their families and healthcare providers who administer BPG, three software applications will be developed from pre-existing applications that have been optimized for use in target populations in New Zealand. This will be the first time software applications have been used to collect qualitative and quantitative data on individual preferences for BPG formulations and dosing regimens in Samoa.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".