The impact of social isolation on the incidence of TBI in Salvador-BA at the second quarter of last five years
Bibliographic record
Abstract
Background and aims: The COVID-19 pandemic caused radical changes in the daily lives of the population. In this context, actions of social isolation - such as limiting the operation of certain commercial establishments and other actions that prevent the crowding of people - were adopted worldwide to avoid the spread of the SARS-CoV-19 virus. Thus, with the advent of radical changes in the dynamics of society, changes in the pattern of hospitalization in large cities for traumatic brain injury (TBI) can be expected. Therefore, this study aims to trace the epidemiological impact of adopting social isolation on the incidence of TBI in the city of Salvador-Ba, comparing its incidence in the second quarter of the last five years. Methods: Descriptive observational, cross-sectional study, composed of secondary data published by the Ministry of Health through DATASUS and extracted from the SUS Hospital Information System (SIH/SUS). A period of five years was selected (second quarter of 2016, 2017, 2018, 2019 and 2020) and hospital morbidity data for intracranial trauma, by place of hospitalization, covering the territory of Salvador. Results: In the second quarter of 2020, 2019, 2018, 2017 and 2016, were registered, respectively: 394, 426, 452, 367, 374 admissions for intracranial trauma in hospitals in Salvador-Bahia. Conclusion: No significant reduction in the number of hospitalizations during the period of social isolation, compared to previous years. This opposes a perspective that hospitalizations for TBI would reduce in the second quarter of 2020, as a result of installation of the quarantine and risk of contamination in hospitals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".