Using the STROBE statement to assess reporting in blindness prevalence surveys in low and middle income countries
Bibliographic record
Abstract
OBJECTIVE: Cross-sectional blindness prevalence surveys are essential to plan and monitor eye care services. Incomplete or inaccurate reporting can prevent effective translation of research findings. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement is a 32 item checklist developed to improve reporting of observational studies. The aim of this study was to assess the completeness of reporting in blindness prevalence surveys in low and middle income countries (LMICs) using STROBE. METHODS: MEDLINE, EMBASE and Web of Science databases were searched on April 8 2016 to identify cross-sectional blindness prevalence surveys undertaken in LMICs and published after STROBE was published in December 2007. The STROBE tool was applied to all included studies, and each STROBE item was categorized as 'yes' (met criteria), 'no' (did not meet criteria) or 'not applicable'. The 'Completeness of reporting (COR) score' for each manuscript was calculated: COR score = yes / [yes + no]. In journals with included studies the instructions to authors and reviewers were checked for reference to STROBE. RESULTS: The 89 included studies were undertaken in 32 countries and published in 37 journals. The mean COR score was 60.9% (95% confidence interval [CI] 58.1-63.7%; range 30.8-88.9%). The mean COR score did not differ between surveys published in journals with author instructions referring to STROBE (10/37 journals; 61.1%, 95%CI 56.4-65.8%) or in journals where STROBE was not mentioned (60.9%, 95%CI 57.4-64.3%; p = 0.93). CONCLUSION: While reporting in blindness prevalence surveys is strong in some areas, others need improvement. We recommend that more journals adopt the STROBE checklist and ensure it is used by authors and reviewers.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".