DDC_Calc, software to diagnose developmental dysplasia of the hip in infants (version 1)
Bibliographic record
Abstract
Background: developmental dysplasia of the hip constitutes the most frequent affection for the development of the musculoskeletal system. In the context of radiographic diagnosis of this disease in infants, digital radiographs offer advantages that could be used for a more accurate diagnosis. Objective: to describe the DDC_Calc software operation to perform radiographic measurements in digital format. Methods: a technological innovation study was carried out during 2019, at the Paquito González Cueto University Pediatric Hospital, in Cienfuegos, specifically in the Orthopedics Service and with the specialists’ contribution from the "Marta Abreu" Central University’ Mathematics, Physics and Computing Faculty, Las Villas. The Viola-Jones artificial vision algorithm was used for the detection of the structures, as well as a rule-based system with a view to a diagnostic suggestion. Results: the software integrated the necessary tools to carry out the measurements used (angles, distances, positions) in the radiographic diagnosis of developmental dysplasia of the hip. Its functionalities, requirements and operation of the application were described, based on a real clinical case, all illustrated with images. Conclusions: the use of the software facilitates the performance of more accurate measurements by the doctor, thus guaranteeing higher quality in the diagnosis and the preservation of the initial radiographic findings, which are very useful in the radiographic follow-up of the condition in time.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".