JoVE Video Dataset
Bibliographic record
Abstract
Hip dysplasia causes major disability in dogs. Treatment options are limited to palliative treatment (e.g., pain relief, physical exercise, lifestyle changes, and weight control) or invasive surgeries such as pelvic osteotomies and total hip arthroplasty. Hence, a strong unmet need exists for an effective and dog-friendly solution that enhances the quality of life of man's best friend. We fill this treatment gap by offering a minimally traumatic and extraarticular, dog-specific, 3-dimensional-printed, hip implant (3DHIP) that restores hip joint stability. The surgical treatment using a 3DHIP implant is less invasive than osteotomies and can be performed bilaterally in one surgical session. The 3DHIP implant extends the dorsal acetabular rim of the dysplastic hip joint thereby increasing coverage of the femoral head and inhibiting joint subluxation with fast recovery. Sufficient access to the dorsal acetabular rim and ventral border of the iliac body together with optimal fitting and fixation of the implant are key steps for a successful 3DHIP implantation and imply the need for a specific approach. The present article aims to showcase this innovative surgical technique with tips and tricks as a surgical manual for implantation of the 3DHIP implant in dogs affected by hip dysplasia.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.426 | 0.272 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".