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Record W4391990391 · doi:10.1055/s-0044-1780161

Multicenter Study of Sellar Reconstruction after Endoscopic Transsphenoidal Resection of Pituitary Tumors

2024· article· en· W4391990391 on OpenAlexaff
Hawa M. Ali, Evelyn M. Leland, Emily Stickney, Christine M. Lohse, Benita Valappil, Andrey Filimonov, Kaitlin Goetschel, Sarah C. Young, Maryam N. Shahin, Davaine Joel Ndongo Sonfack, Sylvie Nadeau, Pierre‐Olivier Champagne, Olabisi Sanusi, Mathew Geltzeiler, Nathan T. Zwagerman, Paul A. Gardner, Eric W. Wang, Georgios A. Zenonos, Garret Choby, Carl H. Snyderman, Jamie J. Van Gompel, Maria Peris‐Celda, Carlos Pinheiro‐Neto

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2024
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsResectionMedicinePituitary tumorsRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Surgical techniques for sellar reconstruction vary from no reconstruction of the sella, use of synthetic materials, autologous grafts and/or vascularized flaps. This study is the first comprehensive multicenter study that attempts to identify the most efficient and least morbid surgical approach to sellar reconstruction. The aim of this study was to conduct a multicenter study comparing the efficacy and postoperative morbidity associated with different sellar reconstruction techniques. Methods: A retrospective chart review of patients who underwent endoscopic transsphenoidal surgery for pituitary tumors from 5 participating sites between January 2021 and March 2023 was performed. Variables included patient demographics, tumor characteristics, intraoperative findings, reconstruction technique, postoperative CSF leak, and 22-item Sino-Nasal Outcome Test (SNOT-22) scores. Onlay techniques included: no reconstruction, mucosal grafts, nasoseptal flaps, or other techniques. SNOT-22 scores were gathered preoperatively and at 1, 3 and 6 months postoperatively. Comparisons of duration of surgery, postoperative complications and SNOT-22 scores by type of onlay reconstruction were evaluated using analysis of variance and Kruskal–Wallis, two-sample t, Wilcoxon’s rank-sum, chi-square, and Fisher exact test. Results: A total of 507 patients from 5 participating sites were identified. Average tumor size was 2.1 cm, and 64% were nonfunctioning. Intraoperative CSF leak was identified in 38% of patients (2/3 low-flow, 1/3 high-flow). Eighty-nine percent underwent onlay reconstruction; 49% were reconstructed with mucosal grafts, 35% with nasoseptal flap, 5% with other onlay techniques. There were only 6 postoperative CSF leaks identified, and therefore statistical analysis could not be performed on this data. No significant differences in postoperative complications and SNOT-22 outcomes were identified based on the type of onlay reconstruction. Nasoseptal flaps were utilized more frequently in the setting of giant pituitary adenomas (>3 cm), medial cavernous sinus wall resection and high-flow intraoperative CSF leaks. Cases that utilized mucosal grafts versus nasoseptal flaps had an overall shorter operating time (183 vs. 240 minutes, p < 0.001). Conclusion: The effectiveness and morbidity of different sellar reconstruction techniques are comparable, though operative times were longer in cases using nasoseptal flaps. Vascularized flaps were utilized more frequently in the setting of larger tumors and high-flow intraoperative CSF leaks. Low postoperative CSF leak rate was detected in this cohort. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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 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)0.002
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.260
Teacher spread0.235 · 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
Published2024
Admission routes1
Has abstractyes

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