COVID-19 #StayAtHome Restrictions and Deep Vein Thrombosis: Case Report
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic triggered countermeasures like #StayAtHome initiatives, which have changed the whole world. Despite the success of such initiatives in limiting the spread of COVID-19 to #FlattenTheCurve, physicians are now confronted with the adverse effects of the current restrictive pandemic management strategies and social distancing measures. OBJECTIVE: We aim to draw attention to the particular importance and magnitude of what may be the adverse effects of COVID-19-related policies. METHODS: We herein report a case of an otherwise healthy 84-year-old woman with deep vein thrombosis (DVT) due to COVID-19-related directives. #StayAtHome policies and consequential social isolation have diminished our patient's social life and reduced her healthy movement behaviors. The patient spent long hours in a seated position while focusing on the intensive flow of media information regarding the pandemic. RESULTS: Reduced mobility due to preventive social isolation during the COVID-19 pandemic was the only identified cause of the DVT. CONCLUSIONS: While evaluating the effect of the COVID-19 pandemic and governmentally implemented containment measures, including social isolation and mobility reduction, adverse events should be considered. Digital approaches might play a crucial role in supporting public health.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".