An assessment of interobserver agreement on lesion size, morphology and clinical phenotype in cutaneous leishmaniasis caused by Leishmania aethiopica in Ethiopia
Bibliographic record
Abstract
Introduction Cutaneous leishmaniasis (CL) remains a major public health challenge, especially in endemic regions like Ethiopia, where an estimated 40,000 new cases occur annually. Effective treatment evaluation for CL relies on consistent clinical assessments, yet variability in lesion descriptions can complicate reliable outcome measures. Methods We conducted an inter-reliability study of clinicians’ evaluations of CL lesion morphology and size at ALERT Hospital, Addis Ababa. Twelve clinicians independently examined 12 patients with parasitologically confirmed CL, each clinician assessing lesion morphology, size, and severity. Results We found high consistency in reporting major morphological categories (e.g., plaques) but significant variability in secondary features like dyspigmentation and scale, as well as mucosal involvement. Lesion size measurements showed limited variability, suggesting its reliability as a potential measure for future clinical trials. Disparities in severity assessments highlight the need for a standardized scoring system in CL. Discussion Our findings underscore the importance of training for consistent, high-quality clinical evaluations of CL and suggests that lesion size could be a reproducible outcome measure in treatment efficacy trials.
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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".