Reporting quality of the 2014 Ebola outbreak in Africa: A systematic analysis
Bibliographic record
Abstract
The objective of this study was to conduct a systematic analysis of the reporting quality of the Ebola Virus Disease (EVD) outbreak in West Africa from 2014-2018 using the Modified STROBE statement. We included studies on the 2014 EVD outbreak alone, limited to those on human patients in Africa. We searched the following databases (MEDLINE, EMBASE, and Web of Science) for outbreak reports published between 2014-2018. We assessed factors potentially associated with the quality of reporting. A total of 69 of 131 (53%) articles within the full-text review fulfilled our eligibility criteria and underwent the Modified STROBE assessment for analyzing the quality of reporting. The Modified STROBE scores of the included studies ranged from 11-26 points and the mean was found to be 19.54 out of 30 with a standard deviation (SD) of ± 4.30. The top three reported Modified STROBE components were descriptive characteristics of study participants, scientific background and evidence rational, and clinical significance of observations. More than 75% of the studies met a majority of the criteria in the Modified STROBE assessment tool. Information that was commonly missing included addressing potential source of bias, sensitivity analysis, further results/analysis such as risk estimates and odds ratios, presence of a flowchart, and addressing missing data. In multivariable analysis, peer-reviewed publication was the only predictor that remained significantly associated with a higher Modified STROBE score. In conclusion, the large range of Modified STROBE scores observed indicates variability in the quality of outbreak reports for EVD. The review identified strong reporting in some areas, whereas other areas are in need of improvement, in particular providing an important description of the outbreak setting and identifying any external elements (potential biases and confounding factors) that could hinder the credibility of the findings.
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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 | 0.249 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".