A Critical Look at a Critical Care Dataset: MIMIC-IV's Construction, Contents, & Consequences
Bibliographic record
Abstract
MIMIC (Medical Information Mart for Intensive Care) is one of the largest, most commonly-used, freely available datasets containing intensive care unit data. I conduct denotative, connotative, and deconstructive readings of the MIMIC-IV dataset through an analysis of the data sources, dataset structure, and the process for getting access to the data, as well as documents and concepts related to the dataset. As a result, I demonstrate that the MIMIC-IV dataset requires more documentation, including an expansion of the existing descriptions, in order to ensure the data is used appropriately and allow for maximum benefit. I make recommendations for future users of the MIMIC-IV dataset, creators of datasets in general, and researchers in the Critical Data Studies field based on my findings.
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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 | 0.024 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".