Risk Factors for Delayed Diagnosis of Scabies in Hospitalized Patients From Long-Term Care Facilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Delayed diagnosis of scabies can cause an institutional outbreak, which causes considerably economic burden to control. This study was to find the risk factors for delayed diagnosis of scabies in hospitalized patients from long-term care facilities. METHODS: We conducted a retrospective analysis of the hospitalized patients from long-term care facilities, diagnosed to have scabies between January 2006 and December 2008. A stepwise logistic regression analysis was performed to determine the risk factors for delayed diagnosis of scabies. RESULTS: A total of 706 episodes with scabies were identified retrospectively in 399 hospitalized patients from long-term care facilities. Of these, 44 episodes were considered as delayed diagnosis of scabies. These patients were more associated with chronic usage of steroid (73% vs. 10%, P < 0.001) and had longer duration of hospitalization than the others (30 vs. 13 days, P < 0.001). After logistic regression, steroid therapy was the risk factor of delayed diagnosis of scabies (odds ratio: 23.493). CONCLUSIONS: In the patients from long-term care facilities, clinical physicians should pay more attention to those with chronic usage of steroid to avoid delayed diagnosis of scabies. KEYWORDS: Scabies; Delayed diagnosis; Risk factor; Long-term care facility.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it