Bibliographic record
Abstract
Although adverse events are pervasive in NICUs, many events are preventable. Most healthcare organizations rely on traditional methods of detecting adverse events, focusing on voluntary reporting by healthcare personnel. It is estimated that only five to ten percent of errors are reported using these methods, and of those, 90% to 95% cause no harm. The use of a Trigger Tool helps identify and target harmful events. In an effort to improve safe care for neonatal patients, a program-specific tool for identifying adverse events with associated harm was developed. The trigger tool will help prioritize focused quality improvement (QI) initiatives to reduce adverse events. The Neonatal trigger tool utilizes an approach where a “triggered event” prompts a detailed review of the account in an effort to determine the occurrence of adverse events and associated harm. Every month, 20 charts are randomly selected from discharges where babies have had a minimum NICU stay of 48 h. Two research nurses review all medical records looking for the presence of program-specific triggers. Once triggers are identified, the chart is independently analyzed on two further occasions by trained team members. These reviews are time sensitive, taking 20 min per chart, and focus on determining the associated adverse events and rating preventability. Quarterly meetings are held to discuss adverse events and any discrepancies in reporting. Data is cumulated biannually based on categories of events. Neonatal QI initiatives are prioritized, focusing on reducing the most harmful and frequently observed adverse events. Data is presented as types of events and graphed into pie charts. In comparison to conventional hospital reporting systems, where 35% of identified errors are medication related, the 2010 trigger tool report indicated medication errors causing harm accounted for 4% of the program's adverse events whereas; airway management, including unplanned extubations and unsuccessful intubations, accounted for 26% of harmful errors. Focused QI initiatives reduced the incidence of airway associated adverse events by 25%, which was sustained over an 18-month period. Focused strategies consisted of purchasing a video laryngoscope, limiting intubations in VLBW infants to skilled intubators, safety learning videos and focused nursing education. The Neonatal trigger tool provides an effective method of identifying harmful adverse events. QI initiatives aimed at reducing patient harm can be implemented and tested.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".