Patterns of paediatric analgesic use in Africa: a systematic review
Bibliographic record
Abstract
We conducted a systematic literature review with two objectives: (1) to assess reported patterns of analgesic use in African children and compare these observed patterns to the analgesics given in the WHO Essential Medicines List for Children (EMLc); and (2) to summarise outcomes related to effectiveness, adverse events, cost and accessibility of these analgesics. Eligible participants were children (≤12 years) living in any African country who received an analgesic administered with the intention of relieving pain in any setting. Thirty-four peer-reviewed, observational studies representing 7772 African children were accepted. Studies were conducted in 25 different regions of 12 countries. Pain was attributed to surgery, burns, sickle cell anaemia and conditions requiring palliation in 32% of children, and was unspecified in the other 68%. Of the three EMLc analgesics, paracetamol and ibuprofen were widely employed, constituting ∼60% of all analgesics, while morphine was used in 20 children (0.2%). There were 455 suspected adverse drug reactions which included 17 deaths. Analgesic use reported in African children appears to fall short of WHO standards.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".