Quality Measurement in Neonatal Surgical Disorders: Development of Clinical Indicators
Bibliographic record
Abstract
Objective This study aims to develop a set of quality indicators for the measurement of the quality of surgical care for neonates with surgical disorders. Methods An expert panel of the Netherlands Association of Pediatric Surgeons developed internal (clinical) indicators for neonatal surgery. This included the selection of appropriate care processes, a review of the scientific literature, consensus meetings to establish national guidelines, selection of clinical indicators with independent external evaluation, the setup of a national database, and a pilot study in one of the hospitals to evaluate the defined quality indicators in clinical practice. Results Seven neonatal surgical care processes were selected. Clinical guidelines to evaluate the care processes were established in six of seven disorders and were based on consensus agreement, which was reached in 81 to 97% of in total 220 relevant items. The expert panel selected a set of 24 indicators to estimate the quality of neonatal surgical care, of which 12 were outcome indicators and 12 process indicators. Conclusion The development of quality indicators is an important step toward monitoring and, if necessary, improving the quality of neonatal surgical care. Internal or clinical indicators guarantee that the results are only disclosed to the participating center itself and are therefore no threat to individual doctors.
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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 | 0.104 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".