The Maastricht Intensive Care COVID Cohort: A Critical Appraisal of the Predefined Research Questions
Bibliographic record
Abstract
IMPORTANCE: A review of the study processes and protocols afterward by the researchers themselves is scarce. OBJECTIVES: The present study aimed to evaluate the study design and the process of data collection of the Maastricht Intensive Care COVID (MaastrICCht) cohort during the COVID-19 pandemic. This evaluation provides information about the quality of the predefined questions and contributes to transparency in science. DESIGN, SETTING, AND PARTICIPANTS: Critical appraisal of studies using data from the MaastrICCht cohort. MAIN OUTCOMES AND MEASURES: Evaluation of the process of study design and data collection during the COVID-19 pandemic, focusing on the research process and results. RESULTS: From March 2020 to April 2023, all patients diagnosed with COVID-19 admitted to the ICU at Maastricht University Medical Center + (n = 544) were included in the MaastrICCht cohort. In total, 37 studies were carried out until April 2024. Fifteen studies addressed 11 of the 13 predetermined research questions, whereas 22 additional studies were performed based on the initial research questions described in the design. Furthermore, 10 studies were conducted with other researchers in national and international collaboration as a response to new arising questions based on evidence that appeared relevant during the pandemic. CONCLUSIONS AND RELEVANCE: Our critical appraisal indicated that using a study protocol enabled many publications and (inter)national collaborations, although formulating pertinent research questions in the context of a novel disease appeared daunting. Despite this, most questions were successfully addressed, whereas few were resolved by other researchers or lost importance due to the expanding body of knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".