Quality of life of cutaneous leishmaniasis suspected patients in the Ecuadorian Pacific and Amazon regions: a cross sectional study
Bibliographic record
Abstract
BACKGROUND: Yearly, up to 1 million patients worldwide suffer from cutaneous leishmaniasis (CL). In Ecuador, CL affects an estimated 5000 patients annually. CL leads to reduced Health Related Quality of Life (HRQL) as a result of stigma in the Asian and Mediterranean contexts, but research is lacking for Ecuador. The objective of this study was to explore the influence of CL suspected lesions on the quality of life of patients in the Pacific and Amazon regions. METHODS: Patients for this study were included in the Amazonian Napo, Pastaza, and Morona Santiago provinces and the Pacific region of the Pichincha province. Participating centers offered free of charge CL treatment. All patients suspected of CL and referred for a cutaneous smear slide microscopy examination were eligible. This study applied the Skindex-29 questionnaire, a generic tool to measure HRQL in patients with skin diseases. All statistical analysis was done with SPSS Statistics version 28. RESULTS: The skindex-29 questionnaire was completed adequately by 279 patients who were included in this study. All patient groups from the Amazon scored significantly (P < 0.01) higher (indicating worse HRQL) on all the dimensions of the Skindex-29 questionnaire than Mestizo patients from the Pacific region. The percentage of patients with health seeking delay of less than a month was significantly (P < 0.01) lower in the Amazon region (38%) than in the Pacific (66%). CONCLUSIONS: The present study revealed that the influence of suspected CL lesions on the HRQL of patients in the Ecuadorian Amazon and Pacific depends on the geographic region more than on patient characteristics such as gender, age, number of lesions, lesion type, location of lesions, health seeking delay, or posterior confirmation of the Leishmania parasite. The health seeking delay in the Amazon might result from a lack of health infrastructure or related stigma. Together, the impaired HRQL and prolonged health seeking delay in the Amazon lead to prolonged suffering and a worse health outcome. Determinants of health seeking delay should be clarified in future studies and CL case finding must be improved. Moreover, HRQL analysis in other CL endemic regions could improve local health 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".