Bibliographic record
Abstract
It is a great pleasure to write a Foreword to this Second Edition of Imaging of the Newborn edited by Drs. Haresh Kirpalani, Monica Epelman, and John Richard Mernagh. Imaging in newborns remains a great intellectual challenge. While many neonatal conditions may be appropriately managed with the aid of plain radiographs, only there are other clinical situations that require more sophisticated procedures such as ultrasound, contrast examinations of the gastrointestinal tract, magnetic resonance imaging, computed tomography, or even interventional procedures. Irrespective of which modality is used, meticulous technique is required in these small patients as the imaging requirements are different from those for older infants and children. Furthermore, in the newborn, special attention to immobilization techniques, temperature control, and ventilation during imaging is also vitally important. Radiological interpretation of imaging examinations in a newborn is worthless without a good clinical history from the neonatologist, neonatal surgeon, or physician responsible for the care of the newborn. A close dialogue between the clinician and radiologist is imperative for accurate and meaningful interpretation of images from all modalities. This is particularly important when radiological findings are non-specific in order to ensure appropriate management and to ensure that the correct decision is made whether to use other modalities to clarify the findings.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.099 | 0.102 |
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".