Model‐based diagnosis with FTTell: Diagnosing early pediatric failure to thrive
Bibliographic record
Abstract
Abstract Pediatric Failure To Thrive (FTT), commonly presented in young infants, is often not diagnosed on time or missed. Lack of timely infants’ diagnosis can adversely affect their growth and development. We have developed and successfully tested FTTell—a model‐based system for diagnosing FTT during common pediatric follow up. FTTell is an executable model‐based diagnostic tool for diagnosing FTT. We use Object‐Process Methodology extended with Methodical Approach to Executable Integrative Modeling, enabling qualitative considerations and quantitative parameters of the problem to be modeled jointly, enabling FTT diagnosis. The validity of FTTell is demonstrated on data collected from 100 infants. For each child, FTTell calculates a score indicating FTT presence and severity. We compared the systems’ outcomes to a pediatric gastroenterologist expert severity assessment. Of the 100 infants, the system initially yielded 82% validity. Reassessment improved it to 87% validity. Pediatricians may miss infants with FTT, especially in borderline cases. FTTell can effectively serve as a FTT diagnosis tool, boosting pediatricians’ correct diagnosis and proper investigation. Our cloud‐based system can be continuously updated with the latest research findings. FTTell can diagnose FTT and its severity in infants with 87% accuracy. Pediatricians can use this model‐based standardized approach to improve their FTT diagnosis and provide appropriate timely intervention when needed. Model‐based diagnosis is a novel application of conceptual models, and OPM ISO 19450 is especially fit for this purpose. The model‐based diagnosis approach can be extended beyond medicine to diagnosing problems with engineered, technological, and socio‐technical systems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".