Identification of Thermal Abnormalities by Analysis of Abdominal Infrared Thermal Images of Neonatal Patients
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC), is a devastating inflammatory disease of infants for which there is no cure and exact causes remain unknown.Diagnoses are limited to radiographic findings and in most institutions Modified Bell's Criteria is used, neither are capable of reliable early detection.In this thesis, a novel method of abdominal infrared thermal imaging is proposed that allows direct measurements of skin temperature, which are capable of unveiling thermal abnormalities that may indicate intestinal inflammation characteristic of NEC.Abdominal thermal symmetry analysis was performed, results obtained from the 20 normal and the 9 NEC affected infants were statistically compared.A higher degree of thermal asymmetry was seen with the NEC group in comparison to the Normal group, notably when image enhancement techniques were done.We are hopeful that this new non-contact, non-ionizing method may potentially offer an early diagnostic tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".