Improvement in neonatal intensive care in Northern Ireland through sharing of audit data
Bibliographic record
Abstract
PROBLEM: Ten percent of infants born will require admission to a neonatal facility. Coordinated activity to monitor and improve the quality of care for this high risk, high cost group of infants is considered a high priority. At the time of initiation of this project no system for collection and analysis of neonatal data existed in Northern Ireland. DESIGN: In 1994 an ongoing prospective centralised data collection system was implemented to facilitate quality improvement and research in neonatal care. We aim to ascertain if there has been a demonstrable improvement in the quality of care provided since the initiation of this system. SETTING: All nine Northern Ireland neonatal intensive care units returned prospectively collected socioeconomic, obstetric and neonatal episode data. KEY MEASURES FOR IMPROVEMENT: Achievement of the agreed quality indicators relating to transfer patterns, thermoregulation, antenatal steroid administration, and timing of administration of surfactant during the period 1 April 1999 to 31 March 2000 were compared with data for the period 1 April 1994 to 31 March 1996. STRATEGIES FOR CHANGE: Monitoring included audit and annual feedback of timely clear and relevant data where results were provided confidentially as standardised reports, together with anonymised comparisons with other similar sized units. Draft recommendations were made at regional level and units were asked to adopt finalized consensus guidelines at the local level and to implement changes to clinical practice. EFFECTS OF CHANGE: The proportion of transfers taking place in utero increased from 26% to 42% and antenatal steroid administration from 68% to 82%. Normothermia on first admission improved from 66% to 71% for inborn infants. The proportion of infants receiving surfactant where the first dose was given within an hour of birth increased from 13% to 66%. LESSONS LEARNT: A multi-professional regional care network can facilitate the development of agreed standards and a culture of regular evaluation leading to quality improvement.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".