Pandemic health consequences: Grasping the long COVID tail
Bibliographic record
Abstract
Emerging evidence suggests that approximately 10% of people who survive Coronavirus Disease 2019 (COVIDAU : PleasenotethatCOVID À 19hasbeendefinedasCoronavirusDisease2019atitsfirst-19) will have lingering symptoms that negatively affect their quality of life, ability to work, and function [1,2].This important group of people with the post-COVID-19 condition may seem small in comparison to the overall number of people with COVID-19 infection [3].However, many patients who survive COVID-19 are likely to have considerable symptom burden, high resource utilization and health service needs, reduced economic productivity, and possibly a shortened life expectancy.The study by Bhaskaran and colleagues published in PLOS Medicine addresses an evolving, poorly studied, and important area of health policy and planning related to the care of patients who survive hospitalization for COVID-19 [4].At face value, the scope of the COVID-19 pandemic is enormous.Within 2 years, nearly 300 million people have been infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARSAU : AU : PleasenotethatSARS À CoV À 2hasbeendefinedasSevereAcuteRespiratorySyndro -CoV-2) virus, and more than 5 million people have died from it [5].But, there is also a long tail to this statistical distribution of hardship.Studies report that numerous patients will continue to experience fatigue, shortness of breath, pain, sleep disturbances, anxiety, and depression [6].More serious organ dysfunction such as pulmonary fibrosis, cognitive impairment, myocarditis, and renal failure may also develop [6].Whether these translate into clinical diagnoses of chronic diseases like interstitial lung disease, dementia, heart failure, and chronic kidney disease remains to be seen.Collectively, the prospect for immense suffering among these individuals will undoubtedly have huge and enduring impacts on healthcare systems globally.As the world continues its largest vaccination effort in history and looks to eliminate the impacts of acute COVID-19, we must not forget that a meaningful minority who survive will transition from an acute to chronic disease state.In turn, management strategies and health resource planning must also appropriately transition.As a multisystem disease, the post-COVID-19 condition will require the involvement of multidisciplinary care teams [7]: Who will help to look after these patients?Bhaskaran and colleagues studied over 164,000 hospitalized adults with COVID-19 matched to an "active control" group of adults hospitalized with influenza and to general population controls.They compared the medium-and long-term risks of hospital admission and death across the 3 study groups.The main findings were that people discharged following hospitalization for COVID-19 had a 2-fold higher associated risk for rehospitalization and death than the general population and similar risks compared to those hospitalized for influenza.These outcomes were most pronounced in the first 30 days following discharge yet remained substantially elevated over time.Further, those hospitalized with COVID-19 were more likely
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".