Desenvolvimento de um sistema web para a notificação e vigilância epidemiológica de trauma com monitorização e análise de indicadores de qualidade do atendimento
Bibliographic record
Abstract
Trauma is a leading cause of death worldwide.It is estimated that more than five million people die annually from some sort of trauma and millions more who survive their injuries are left with temporary or permanent sequelae, which leads to billions of Reais in direct and indirect costs.Thus, the question of trauma involves epidemiological, social, healthcare, financial and management issues.One way to lessen such problems is to evaluate the phases of medical care through quality improvement programs.The American College of Surgeons Committee on Trauma has created a unique aggregation of trauma registry data from several centers in the United States and Canada in a single database, the National Trauma Data Bank (NTDB).After collected, the data are processed into annual reports with indicators that provide a view of the overall situation of trauma care nationwide.Many countries invest resources on gathering trauma registries or building regional databases, which are important sources of data for generating care quality indicators.In Brazil there is no systematic notification of trauma patients in health services.The present study aims to develop a software with a trauma notification and epidemiological surveillance module associated with the monitoring and analysis of the consolidated data using care quality indicators.To test the software we used the database of trauma patients treated at the Emergency Unit of the Clinics Hospital at the Ribeirão Preto Medical School -University of São Paulo (UE HCFMRP / USP) from 2006 to 2014.There are two ways to feed the software with the trauma data: manually, by completing an electronic notification form or by directly importing an Excel file with the same data stream.The indicators are then generated automatically and can be viewed in charts and tables.The results yielded from the software were used to assess the situation of trauma healthcare in the Ribeirão Preto region.The analysis of such results was also crucial to determine the software capacity to provide relevant information for hospital management.The results analysis led us to conclude that the software can help assess the quality of trauma healthcare.A possibility of system expansion is to include new indicators and collect data from other institutions to allow external benchmarking.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".