Getting to 2020: A Clinical Review of the Diagnosis, Treatment and Prevention of Trachoma
Bibliographic record
Abstract
Objectives: 1) to undertake a review of the literature to provide information to clinicians and trainees about the epidemiology, treatment and most importantly, prevention, of trachoma - the leading infectious cause of blindness worldwide. 2) To highlight the diagnosis and treatment of this preventable infectious eye disease for trainees and to evaluate the efficacy of the SAFE treatment and prevention strategy endorsed by the WHO as the target trachoma elimination year of 2020 nears. Methods: A review of the literature was undertaken. PubMED, Clinical Key, UpToDate and Google Scholar databases were searched using the following MeSH terms and keywords: trachoma, infectious eye disease, chlamydia trachomatis, SAFE, community prevention. Results: The diagnosis of trachoma is typically clinical and is made by identifying the signs of conjunctival inflammation and scarring, trichiasis and corneal opacification in the context of a trachoma-endemic region. Randomized controlled trials have demonstrated the efficacy of the WHO’s SAFE (Surgery, Antibiotics, Facial Cleanliness, Environmental change) treatment and prevention strategy, the implementation of which has been associated with declining rates of trachoma worldwide.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".