Design, calibration and evaluation of a robotic needle-positioning system for small animal imaging applications
Bibliographic record
Abstract
A needle-positioning robot has been developed for image-guided interventions in small animal research models. The device is designed to position a needle with an error < or =100 microm. The robot has two rotational axes (pitch and roll) to control needle orientation, and one linear axis to perform needle insertion. The three axes intersect at a single point to create a remote centre of motion (RCM) that acts as a fulcrum for the orientation of the needle. The RCM corresponds to the skin-entry point of the needle into the animal. The robot was calibrated to ensure that the three axes intersected at a single point defining an RCM and that the needle tip was positioned at the RCM. Needle-positioning accuracy and precision were quantified in Cartesian coordinates at ten target locations in the plane of each rotational axis. The measured needle-positioning accuracy in free space was 54 +/- 12 microm for the pitch axis plane and 91 +/- 21 microm for the roll axis plane. The measured needle-positioning precision was 15 and 17 microm for the pitch and roll axes planes, respectively. The robot's ability to insert a needle into a tumour in a euthanized mouse was demonstrated.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".