Anemia in Cambodia: prevalence, etiology and research needs.
Bibliographic record
Abstract
Anemia is a severe global public health problem with serious consequences for both the human and socio-economic health. This paper presents a situation analysis of the burden of anemia in Cambodia, including a discussion of the country-specific etiologies and future research needs. All available literature on the prevalence and etiology of anemia in Cambodia was collected using standard search protocols. Prevalence data was readily identified for pre-school aged children and women of reproductive age, but there is a dearth of information for school-aged children, men and the elderly. Despite progress in nation-wide programming over the past decade, anemia remains a significant public health problem in Cambodia, especially for women and children. Anemia is a multifaceted disease and both nutritional and non-nutritional etiologies were identified, with iron deficiency accounting for the majority of the burden of disease. The current study highlights the need for a national nutrition survey, including collection of data on the iron status and prevalence of anemia in all population groups. It is impossible to develop effective intervention programs without a clear picture of the burden and cause of disease in the country.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".