The influence of socioeconomic deprivation on outcomes in transplant patients infected with SARS‐CoV‐2 in Wales
Bibliographic record
Abstract
INTRODUCTION: SARS-CoV-2 infection has had a significant impact on vulnerable individuals including transplant patients. Socioeconomic deprivation negatively affects outcomes of many health conditions. The aim of this study was to evaluate the effect of socioeconomic deprivation on the incidence and severity of SARS-CoV-2 infection among Welsh transplant patients. METHODS: This study is a retrospective, cross-sectional study on the transplant population of Wales. The Welsh Index of Multiple Deprivation (WIMD) was used to assess the influence of socioeconomic deprivation on outcomes of Welsh transplant patients who developed SARS-CoV-2 infection. Outcome measures were the incidence of SARS-CoV-2 infection, rates of hospital and ICU admission, development of acute kidney injury (AKI) and mortality. A logistic binomial regression analysis was used to correlate the various risk factors with the incidence of SARS-CoV-2 infection. RESULTS: Two hundred and sixty-six (25%) of regular follow up patients had SARS-CoV-2 infection; of these 55 (20.7%) were admitted, 15 (5.6%) to ICU, 37 (13.9%) developed AKI, and 23 (8.6%) died. In a regression analysis, patients of younger age were associated with more (p = .001) and those with SPK (simultaneous pancreas kidney) transplant less chance of infection (p = .038), whereas social deprivation was not associated with the chance of infection (p = .14). In regression analysis increased social deprivation was associated with higher chance of AKI post SARS-CoV-2 (p = .049). CONCLUSIONS: Socioeconomic deprivation did not affect the rates or severity of SARS-CoV-2 infection apart from the degree of AKI in Welsh Transplant patients. Adherence to the preventive measures for this high-risk population must continue to remain a priority.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".