Tafenoquine for the radical cure versus prophylaxis of Plasmodium vivax malaria: the importance of using the appropriate data set
Bibliographic record
Abstract
Background & objectives: The post-acute effects of COVID-19 are continually being updated. This investigation was conducted to evaluate the determinants of post discharge mortality in hospitalized COVID-19 patients, especially 18-45 yr of age. Methods: A series of three nested case-control analyses was conducted on follow up data collected in the National Clinical Registry for COVID-19 between September 2020 and February 2023 from 31 hospitals. Matching (1:4) was done by the date of hospital admission ±14 days for the following comparisons: (i) case-patients reported as dead vs. controls alive at any contact within one year follow up; (ii) the same in the 18-45 yr age group and (iii) case-patients reported as dead between the first and one year of follow up vs. controls alive at one year post discharge. Results: The one year post discharge mortality was 6.5 per cent (n=942). Age [≤18 yr: adjusted odds ratio (aOR) (95% confidence interval [CI]): 1.7 (1.04, 2.9); 40-59 yr: aOR (95% CI): 2.6 (1.9, 3.6); ≥60 yr: aOR (95% CI): 4.2 (3.1, 5.7)], male gender [aOR (95% CI): 1.3 (1.1, 1.5)], moderate-to-severe COVID-19 [aOR (95% CI): 1.4 (1.2, 1.8)] and comorbidities [aOR (95%CI): 1.8 (1.4, 2.2)] were associated with higher odds of post-discharge one-year mortality, whereas 60 per cent protection was conferred by vaccination before the COVID-19 infection. The history of moderate-to-severe COVID-19 disease [aOR (95% CI): 2.3 (1.4, 3.8)] and any comorbidities [aOR (95% CI): 3 (1.9, 4.8)] were associated with post-discharge mortality in the 18-45-yr age bracket as well. Post COVID condition (PCC) was reported in 17.1 per cent of the participants. Death beyond the first follow up was associated with comorbidities [aOR (95%CI): 9.4 (3.4, 26.1)] and reported PCC [aOR (95% CI): 2.7 (1.2, 6)]. Interpretation & conclusions: Prior vaccination protects against post discharge mortality till one year in hospitalized COVID-19 patients. PCC may have long term deleterious effects, including mortality, for which further research is warranted.
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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 | 0.112 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".