Bibliographic record
Abstract
BACKGROUND: Scabies is globally ubiquitous and is a significant health issue for institutions, the economically disenfranchised, resource-poor areas, and for those with weakened immune systems. Topicals are usually effective, but are cumbersome and expensive to use in large populations and for those nonadherent to topicals. Oral ivermectin became available in Canada for the off-label treatment of scabies in the fall 2018. OBJECTIVES: To review the diagnosis and management of scabies. Dose schedules and concomitant management measures are outlined for scabies simplex and for crusted scabies. Ivermectin use is outlined. METHODS: Medline, colleague discussions, practice review, and experience from managing scabies in institutions. RESULTS: Oral ivermectin is safe, easier to use, cheaper, more effective, and more economical than topicals in widespread institutional scabies, for those nonadherent to topicals, and in crusted scabies. CONCLUSIONS: Oral ivermectin is the treatment of choice in large populations, the nonadherent, and for crusted scabies. Oral ivermectin is produced by Merck Canada as Stromectol 3 mg. The treatment dose for noncrusted scabies is 200 µg/kg, taken in a single dose with food. For example, 15 mg (5 tablets) for a 70 kg person. Retreat in 10-14 days to enhance effectiveness, and perhaps to reduce scabicide resistance.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".