Bibliographic record
Abstract
Scabies is a contagious skin infestation caused by a mite. It causes significant global morbidity, with an estimated 300 million cases annually. Although it can affect individuals at any socioeconomic level, individuals who live in poverty or in overcrowded conditions are at much higher risk for scabies. Lack of local expertise can result in failure to recognize scabies, leading to delayed diagnosis and inadequate treatment of cases and contacts. Scabies disproportionately affects many Indigenous (First Nations, Inuit, Métis) communities where risk factors are present. Scabies risk is also higher in young children, the elderly and immunocompromised individuals. Institutional outbreaks of scabies have also been reported. Apart from a very itchy rash, scabies can lead to secondary bacterial infections and related complications, as well as to stigmatization, depression, insomnia and significant financial costs. Topical antiscabies lotions are still the mainstay of treatment, but oral ivermectin has also proven effective under certain circumstances. Asymptomatic and symptomatic household members should all be treated at the same time. In Canada and globally, the presence of scabies is usually a symptom of poor living conditions and a sign that basic necessities need improvement. Clinicians who work with Indigenous communities can improve their ability to diagnose and treat scabies, and should advocate for better living conditions where scabies is prevalent.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".