Long covid syndrome: Why efforts like aftercor are needed
Bibliographic record
Abstract
Introduction: The AFTERCOR study was developed by the COVID-19 Critical Care Consortium (>7000 intensive care unit [ICU] and >400 extracorporeal membrane oxygenation [ECMO] patients currently) to enhance understanding of occurrence and progression of long-term dysfunction post-COVID-19. Design: Prospective longitudinal (24 months) study of ICU survivors of COVID-19 to describe recovery of the following aspects: a) health-related quality of life b) dynamics of organ dysfunction and recovery and c) pulmonary function. Countries involved Italy, Spain, Ireland, Austria, South Africa, Australia, USA, Argentina, Brazil, Colombia. Protocol specifics available at https://www.aftercorstudy.com. Inclusion Criteria: 1) COVID-19 infection requiring ICU admission;2) informed consent;3) age ≥18 years. Exclusion Criteria: 1) pregnancy;2) pre-COVID paralysis;3) history of pulmonary resection;4) prior lung transplant;5) inability to perform 6-min walk test or participate in interview. Methods: Goal enrollment is 1000 patients. Follow-up visits are at 3, 6, 12, 18 and 24-month post-ICU discharge. Assessments include: 1) Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36);2) Montreal Cognitive Assessment;3) any subsequent admission 4) St. George's Respiratory Questionnaire;5) Pulmonary function testing;6) chest radiography;7) 6-minute-walk test;8) Patient Health Questionnaire 9 (PHQ-9) and 9) full blood count and biochemistry. CT chest at 6 months and repeat ECHO at 3, 12 and 24 months if performed during COVID-19 hospitalization. If results are normal, subsequent testing will not be performed. Summary: The AFTERCOR study represents a comprehensive evaluation for long-term effects from COVID-19. Interested centers are sought and invited to participate.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".