Limitations of using the DATASUS database as a primary source of data in surgical research: a scoping review
Bibliographic record
Abstract
OBJECTIVE: DATASUS is the Brazilian Public Unified Health System (SUS) department responsible for providing health data that are used as a primary source of data in several studies on surgery and surgical specialties although its main limitations have not been previously reviewed. The objective of this work is to synthesize information from studies on surgery that used DATASUS systems as a data source and to identify the main gaps in this platform. METHODS: a scoping review was conducted according to the PRISMA-ScR method to identify papers on surgery, and other surgical specialties, that used the DATASUS platform as a primary data source. No restrictions were imposed regarding the type of study or year of publication. Grounded Theory was used to analyze the content of the articles. RESULTS: 248 works were initially analyzed and 47 were included in the final analysis of this study. The original articles included were published between 2009 and 2022 and the majority (12.76%, n=6) were published in the Journal of the Brazilian College of Surgeons. Retrospective studies (40.43%, n=19) were the most common type of study found. Content analysis of the articles identified four predominant domains in the scientific literature about the limitations of using DATASUS in surgical research: lack of data, reliability, precision and data integration. CONCLUSION: the information systems available in DATASUS are the largest source of information about the SUS, but the scientific literature on the quality of data available in these systems remains scarce and studies aimed at measuring this metric are necessary.
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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 | 0.285 | 0.613 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.039 | 0.038 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".