Prognostication in neonatal hypoxic ischemic encephalopathy; A qualitative research study
Bibliographic record
Abstract
AbstractBackgroundHypoxic ischemic encephalopathy is the most frequent cause of neonatal encephalopathy, and results in significant morbidity and mortality. From an ethical and clinical standpoint, neurological prognosis is fundamental in the care of neonates with hypoxic ischemic encephalopathy. However, accurately predicting neurodevelopmental outcomes for neonatal hypoxic ischemic encephalopathy is particular difficult, and fraught with challenges. At present, focused research in this area is limited. ObjectivesThis thesis aims to present a review of the current literature on prognosis and the practice of prognostication in neonatal hypoxic ischemic encephalopathy, focusing on the integral challenges posed by this vulnerable group of neonates. Furthermore, this thesis incorporates an original qualitative study that explores physician perspectives about prognostication in neonatal hypoxic ischemic encephalopathy. The main objective of this thesis is to advance the current understanding of the practice of prognostication in neonatal hypoxic ischemic encephalopathy, in hopes of opening up dialogue and encouraging modifications in clinical practice to improve patient care. MethodsThe introduction and background section of this thesis presents a review of the literature on prognosis, prognostication, and neonatal hypoxic ischemic encephalopathy. Focus is placed on selected articles in an attempt to introduce the reader to the subject mater. The research publication included in this thesis is based on a Canadian qualitative study of neonatologists and pediatric neurologists, which focuses on exploring physician perspectives on prognosis and prognostication in neonatal hypoxic ischemic encephalopathy. For the purpose of this thesis, only data pertaining to the practice of prognostication will be presented, though there were other themes explored in the larger research study including uncertainty, communication, and shared-decision-making. ResultsThere are two chapters presenting the results in this thesis. The first consists of a published manuscript that reports on data regarding physician perspectives on prognostication in neonatal hypoxic ischemic encephalopathy, and the second presents unpublished data exploring the challenges of prognostication in neonatal hypoxic ischemic encephalopathy. ConclusionsAlthough this thesis offers some practical changes for the improvement of clinical care in neonatal HIE, its true contribution is to serve as a launching point for further research. Focused research is needed to explore both the parental perspective and, prospectively, the impact of different clinical approaches and styles to prognostication for neonatal hypoxic ischemic encephalopathy.
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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 | 0.042 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".