Response
Bibliographic record
Abstract
In response to Ding's comments regarding the age-dependent curve fitting, we agree that a biexponential might be better suited to fit T2 data extending into the second decade of life. However, our age range was limited to 4 years, 5 months and as clearly stated in the text, “…insufficient data from older subjects may impede the detection of a second exponential term.” In addition, it is clear from Table 2 that in some regions of interest the biexponential did indeed slightly increase the quality of fit (adjusted R2 value). However, because the fitting failed to converge in the two regions of the corpus callosum and the thalamus, for the sake of consistency we chose to use a monoexponential fit. We also believe that different regions of interest might exhibit different age-dependent behaviors, as shown by the diffusion tensor imaging study referred to by Ding, where the genu of the corpus callosum showed a mono- rather than a biexponential decay with age (1). Another point to consider is the significant variability in T2 values for very young subjects, particularly in frontal white matter. This variability, combined with a generally low number of subjects for this age group, can limit the detection of subtle features in the fits. In any case, these are empirical fitting attempts with no theoretical underpinnings. Many other mathematical functions could have fitted the data well. These are purely descriptive functions and no theory about development depends on their validity. With regard to the acquisition method, it is well known that there are multiple components of T2 and that a two-echo calculation of T2 has limitations, in particular when done with a multislice sequence on a clinical scanner. It is unclear whether a triple-echo sequence provides significantly more accurate T2 estimates than two dual-echo sequences. In any event, because of the extensive protocol of the full study (2), the time limitations often did not even permit the second dual-echo data to be acquired. We also stress that these acquisitions, in fact, only provide pseudo-T2 values since they are based on fast spin-echo sequences. The important point is that these pseudo-T2 measurements are reproducible and reflect developmental changes in brain tissue. We believe these acquisition differences would not significantly affect the overall behavior of estimated T2 with age, particularly when considering the large change in T2 values for the age range considered. Ilana R. Leppert MEng*, C. Robert Almli PhD , Robert C. McKinstry MD, PhD , Robert V. Mulkern PhD?, Carlo Pierpaoli MD,PhD?, Micheal J. Rivkin MD**, G. Bruce Pike PhD , * Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, Developmental Neuropsychobiology Laboratory, Departments of Neurology, Psychology, Programs in Neuroscience, Occupational Therapy, Washington University School of Medicine, St. Louis. Missouri, USA, Mallinckrodt Institute of Radiology and, St. Louis Children's Hospital, Washington University Medical Center, St. Louis. Missouri, USA, ? Department of Radiology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA, ? National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA, ** Department of Neurology, Psychiatry and Radiology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA, Montreal Neurological Institute McGill University Montreal, Quebec, Canada.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.158 | 0.091 |
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".