Neonatal Brain Volumetric Studies: Regression Analysis and Interpretation
Bibliographic record
Abstract
To the Editor.—Volumetric techniques are increasingly used to investigate the impact of injury or intervention on the neonatal brain. Parikh et al1 recently reported in an interesting article that postnatal dexamethasone therapy is followed by reduced cerebral tissue volumes. In view of relatively poor MRI signal contrast between gray and white matter in neonates, researchers overcome challenges in regard to image acquisition and segmentation techniques. However, there remains a big challenge in how best to analyze and interpret the volumes measured. When interpreting the results of neonatal brain volumetric studies, there is often a need to address possible confounding factors. The decision on what confounding factors/covariates to include in the regression model is critical for the conclusions of the study. Equally critical is the way that these possible confounding factors are being tested for and analyzed in the regression model.With regard to the hypothesis tested, previous scientific evidence is usually a good way to start finding which possible covariates to test for. In a landmark article in 1998, Hüppi et al2 addressed clinical parameters that correlate with neonatal cerebral volumes. Moreover, depending on the selection criteria and hypotheses, different samples may display different significant covariates. The statistical significance of group differences on demographic and clinical characteristics may be of some help but is limited. Factors not significantly different between groups may well be proven statistically significant when entered in the regression model to test the primary hypothesis, and vice versa. Also, the influence of a covariate on the primary outcome measure may partially overlap with the influence of a different covariate, which makes the combination tested crucial.In regard to the regression model, it is a good exploratory approach to enter and test the possible covariates initially 1 by 1. This can be followed by a stepwise regression that involves the more statistically significant ones. It is safer to use larger P values (eg, .1 or .2) for entry criteria in the stepwise regression than for exit (eg, .05 or .1). The covariates that have the most influence on the primary outcome (cumulative effect if >1) should stay in the model, and then the primary outcome measurements can be adjusted accordingly. It is important to note whether comparisons for the primary outcome were statistically significant before and after the regression analysis.With respect to the above and in terms of cortical volume, along with postmenstrual age at scan, scaling effects and size differences between subjects and groups at scan could have an influence on neonatal cerebral volumes and be a significant covariate. Also, uncomplicated germinal matrix-intraventricular hemorrhage could be a significant covariate for the cortical volume.3 For the reader, understanding is benefited from adequate details on the demographic and clinical characteristics of the subjects. The presentation of the raw data and their statistics, before adjustments, is also essential for the interpretation of neonatal brain volume studies.
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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 | 0.024 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".