The assessment of bulging fontanel and splitting of sutures in premature infants: an interrater reliability study by the Hydrocephalus Clinical Research Network
Bibliographic record
Abstract
OBJECT: Previous studies from the Hydrocephalus Clinical Research Network (HCRN) have shown a great degree of variation in surgical decision making for infants with posthemorrhagic hydrocephalus, such as when to temporize, when to shunt, or when to convert. Since much of this clinical decision making is dictated by clinical signs of increased intracranial pressure (including bulging fontanel and splitting of sutures), the authors investigated whether there was variability in how these signs were being assessed by neurosurgeons. They wanted to answer the following question: is there acceptable interrater reliability in the neurosurgical assessment of bulging fontanel and split sutures? METHODS: Explicit written definitions of "bulging fontanel" and "split sutures" were agreed upon with consensus across the HCRN. At 5 HCRN centers, pairs of neurosurgeons independently assessed premature infants in the first 3 months of life for the presence of a split suture and/or bulging fontanel, according to the a priori definitions. Interrater reliability was then calculated between pairs of observers using the Cohen simple kappa coefficient. Institutional board review approval was obtained at each center and at the University of Utah Data Coordinating Center. RESULTS: A total of 38 infants were assessed by 13 different raters (10 faculty, 2 fellows, and 1 resident). The kappa for bulging fontanel was 0.65 (95% CI 0.41-0.90), and the kappa for split sutures was 0.84 (95% CI 0.66-1.0). No complications from the study were encountered. CONCLUSIONS: The authors have found a high degree of interrater reliability among neurosurgeons in their assessment of bulging fontanel and split sutures. While decision making may vary, the clinical assessment of this cohort appears to be consistent among these physicians, which is crucial for prospective studies moving forward.
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 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 | 0.090 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".