The Accuracy of Administrative Data for Identifying the Presence and Timing of Admission to Intensive Care Units in a Canadian Province
Bibliographic record
Abstract
BACKGROUND: A prerequisite for using administrative data to study the care of critically ill patients in intensive care units (ICUs) is that it accurately identifies such care. Only limited data exist on this subject. OBJECTIVE: To assess the accuracy of administrative data in the Canadian province of Manitoba for identifying the existence, number, and timing of admissions to adult ICUs. RESEARCH DESIGN: For the period 1999 to 2008, we compared information about ICU care from Manitoba hospital abstracts, with the criterion standard of a clinical ICU database that includes all admissions to adult ICUs in its largest city of Winnipeg. Comparisons were made before and after a national change in administrative data requirements that mandated specific data elements identifying the existence and timing of ICU care. RESULTS: In both time intervals, hospital abstracts were extremely accurate in identifying the presence of ICU care, with positive predictive values exceeding 98% and negative predictive values exceeding 99%. Administrative data correctly identified the number of separate ICU admissions for 93% of ICU-containing hospitalizations; inaccuracy increased with more ICU stays per hospitalization. Hospital abstracts were highly accurate for identifying the timing of ICU care, but only for hospitalizations containing a single ICU admission. CONCLUSIONS: Under current national-reporting requirements, hospital administrative data in Canada can be used to accurately identify and quantify ICU care. The high accuracy of Manitoba administrative data under the previous reporting standards, which lacked standardized coding elements specific to ICU care, may not be generalizable to other Canadian jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".