Hydrodissection of Wiltse’s Plane to Facilitate Exposure During Minimally Invasive Transforaminal Lumbar Interbody Fusion
Bibliographic record
Abstract
Traditional posterior lumbar approaches in a transforaminal lumbar interbody fusion (TLIF) require subperiosteal dissection of bilateral paraspinal muscles to provide adequate exposure. This may traumatize the multifidus muscle and its afferent innervations leading to postoperative paraspinal muscle atrophy. Minimizing such intraoperative trauma has been identified as an important factor in the reduction of postoperative lumbar pain. An approach via a blunt dissection through Wiltse's plane, which lies between the longissimus and multifidus muscles, may minimize postoperative pain. Definition of this plane may be facilitated by local injection of 1% lidocaine within the plane itself, as well as in the musculature defining its borders. In this paper, we demonstrate this technique with a 55-year-old female patient who presented with left-sided radicular leg pain in an L5 distribution. Wiltse plane hydrodissection was utilized in performing an L4-5 TLIF. Ultrasound images of the patient's sub-fascial musculature were obtained pre- and posthydrodissection to assess the elucidation of this plane through this technique. Intraoperative images were obtained following dissection of Wiltse's plane to further illustrate the facilitation of exposure of Wiltse's plane through hydrodissection. Postoperatively the patient did well citing a complete resolution of her radicular pain. She did not require intravenous (IV) pain medication, as her postoperative pain was well controlled with oral pain medication. She was mobilized on post-op day one, and discharged home on post-op day two with minimal back pain. Our initial experience supports the feasibility, safety, and effectiveness of hydrodissection of Wiltse's plane to facilitate exposure during a minimally invasive TLIF and thereby reducing postoperative pain.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".