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.
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 | 0.618 | 0.805 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".