Validation of a low-cost adjustable, handheld needle guide for spine interventions
Bibliographic record
Abstract
Purpose: MR-guided injections are safer for the patient and the physician than CT-guided interventions but require a significant amount of hand-eye coordination and mental registration by the physician. We propose a low-cost, adjustable, handheld guide to assist the operator in aligning the needle in the correct orientation for the injection. Methods: The operator adjusts the guide to the desired insertion angle as determined by an MRI image. Next, the operator aligns the guide in the image plane using the horizontal laser and level gradient. The needle is inserted into the sleeve of the guide and inserted into the patient. To evaluate the method, two operators inserted 5 needles in two facet joints of a lumbar spine phantom. Insertion points, final points and trajectory angles were compared to the projected needle trajectory using an electromagnetic tracking system. Results: On their first attempt, operators were able to insert the needle into the facet joint 85% of the time. On average, operators had an insertion point error of 2.92 ± 1.57 mm, a target point error of 3.39 ± 2.28 mm, and a trajectory error of 3.98 ± 2.09 degrees. Conclusion: A low-cost, adjustable, handheld guide was developed to assist in correctly positioning a needle in MR-guided needle interventions. The guide is as accurate as other needle placement assistance mechanisms, including the biplane laser guides and image overlay devices when used in lumbar facet joint injections in phantoms.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".