Irish National ICU Audit annual report: data from 1st January 2022 to 31st December 2022
Bibliographic record
Abstract
This report documents the work undertaken across the national network of Intensive Care Units (ICUs) and High Dependency Units (HDUs) caring for critically ill patients. Audit of this activity is essential for planning and resource allocation and in order to assure the quality of the care provided. Data coverage in participating Units during 2022 is illustrated in Table 1.1 (Chapter 1). There were significant gaps in quarters of data coverage. The most common reasons for these gaps were inadequate staffing of ICU Audit and the backlog caused by the recent COVID-19 pandemic and the infrastructural cyberattack on the Health Service Executive (HSE). The quarterly data submitted by all Units provide full coverage of the ICU activity for the quarter reported. This level of data coverage supports the comparison between Units based on the assumption that similar trends for a Unit would be observed over the course of each quarter in the year. Where it is necessary to compare counts of data items across Units in this report, counts for Units that had gaps in data were augmented through extrapolation to provide full-year estimates. Throughout this report, such uplifts are clearly labelled as ‘estimated’, and shown in lighter shading in bar/column charts.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.033 |
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".