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Record W4407243229 · doi:10.1055/s-0045-1803140

A Surgical Approach and Institutional Experience with Partial Labyrinthectomy Petrous Apicectomy Surgery: A Technical Description and Summary of Institutional Experience of 66 Cases

2025· article· en· W4407243229 on OpenAlexaff
Celine Hounjet, Jeremy Kam, Serge Makarenko, Benjamin Brakel, Brian D. Westerberg, Ryojo Akagami

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2025
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPetrous boneMedicineSurgeryGeneral surgery

Abstract

fetched live from OpenAlex

Introduction: Partial labyrinthectomy petrous apicectomy (PLPA) is a transpetrous route for approaching clival pathology that is intended to preserve hearing while offering the advantages of labyrinthectomy. A single-surgeon team composed of a fellowship-trained skull base neurosurgeon, otolaryngologist, and neurophysiologist at a tertiary-care center have completed a total of 66 cases utilizing this approach. Methods: A description, diagrams, and intraoperative imaging of this surgical approach utilized at our center is provided. Four handed surgery is performed using a diploscope which has been shown to reduce operating time, blood loss, and improve resection rates in skull base surgery. A cosmetic superficial mastoidectomy is performed prior to drilling, allowing for replacement with titanium fixation. Removal of the lateral and superior semicircular canals is performed by the otolaryngology team in addition to exposure of the sigmoid sinus and petrous apicectomy offering a robust exposure that minimized required retraction throughout the procedure. Intraoperative neurophysiological monitoring is utilized in all cases but tailored to the requirements of the patient. Typically for pathology in this region EMGs/motor evoked potentials of the relevant cranial nerves, four limb SSEPs and MEPs, and brainstem auditory-evoked responses are utilized. Additionally, a chart review was completed on all PLPA cases performed by our surgical team for patient’s demographic, clinical, pathological, and radiographic information pre- and postoperatively. Results: A total of 66 cases, aged 16 to 74 years, were completed using a PLPA approach from 2002 to 2023 by our surgical team. The most common pathology approached with a PLPA exposure at our institution was petroclival meningioma totalling 44/66 (66.7%) of cases. Other pathology approached via PLPA at our institution include epidermoid tumors: 13/66 (19.7%), vestibular schwannoma: (3/66) 4.5%, trigeminal schwannoma: 2/66 (3%), and 1/66 (1.5%) each of facial nerve and solitary fibrous tumors. The average diameter of these lesions was 4.4 cm (range: 1.3–7.2 cm). A gross total resection was achieved in 25/66 (37.8%), near total, defined as >90% tumor resection was achieved in an additional 14/66 (21.2%). An attempt to preserve hearing was made in all patients with hearing at preoperative assessment totalling 44/65 patients. Of these patients, hearing was preserved in 31/44 (70.45%). There were 27/66 (40.1%) cranial nerve deficits. There were 2 of 66 (3%) CSF leaks that required readmission to hospital. One required operative repair. 27 of 66 (40.1%) patients had postoperative cranial nerve deficits. Four of 66 (6%) patients had new motor deficits postoperatively. Eleven of 66 (16.7%) had transient worsening of existing hemiparesis postoperatively. All cases improved. There was a single case of postoperative dysphasia (1.5%), attributed to temporal lobe retraction. There were zero mortality at 90 days postoperative. Conclusion: Use of a PLPA approach is safe and robust in properly selected patients with reasonable hearing preservation. Modifications to surgical technique can help improve the safety and efficacy of this approach. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.044
GPT teacher head0.282
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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