Features of human scabies in resource-limited settings: the Cameroon case
Bibliographic record
Abstract
BACKGROUND: The persistent high prevalence of human scabies, especially in low- and middle-income countries prompted us to research the sociodemographic profile of patients suffering from it, and its spreading factors in Cameroon, a resource-poor setting. METHODS: We conducted a cross-sectional survey from October 2011 to September 2012 in three hospitals located in Yaoundé, Cameroon, and enrolled patients diagnosed with human scabies during dermatologists' consultations who volunteered to take part in the study. RESULTS: We included 255 patients of whom 158 (62 %) were male. Age ranged from 0 to 80 years old with a median of 18 (Inter quartile range: 3-29) years. One to eight persons of our patients' entourage exhibited pruritus (mean = 2.1 ± 1.8). The number of persons per bed/room varied from 1 to 5 (mean = 2.1 ± 0.8). The first dermatologist's consultation occurred 4 to 720 days after the onset of symptoms (mean = 77.1 ± 63.7). The post-scabies pruritus (10.2 % of cases) was unrelated to the complications observed before correct treatment (all p values > 0.05), mainly impetiginization (7.1 %) and eczematization (5.9 %). CONCLUSION: Human scabies remains preponderant in our milieu. Populations should be educated on preventive measures in order to avoid this disease, and clinicians' knowledges must be strengthened for its proper diagnosis and management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".