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Record W4391914703 · doi:10.1097/ms9.0000000000001847

Surgical outcomes for carotid body tumour resection without preoperative embolization: a 10-year experience

2024· article· en· W4391914703 on OpenAlexaff
Barzany Ridha, Varin Aram, Aram Baram, Soren Younis Hama Baqi, Fitoon Yaldo

Bibliographic record

VenueAnnals of Medicine and Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineEmbolizationSurgical resectionSurgeryResectionCarotid bodyPreoperative careRadiologyGeneral surgeryCarotid arteries

Abstract

fetched live from OpenAlex

Background: Carotid body tumours (CBTs) are neoplasms originating from the paraganglionic cells of the carotid body. Excision is the main route of treatment. This study sought to assess the surgical outcomes of post-carotid body tumour resection without preoperative embolization and discern any underlying relationships between modified Shamblin classes (MSC) and related complications. Methods: A retrospective medical record review of prospectively collected data is performed at Sulaymaniyah Teaching Hospital between 2008 and 2019, for 54 patients. Presurgical and postsurgical variables such as comorbidities and complications were noted, respectively. Results: Patient ages ranged between 26 and 60 years (x̄=40.06) with a minimal female predominance (57.4%). Complications included one minor stroke. MSC and postoperative complications were significantly related ( P ≤0.001). Our analyses also suggested a significant relationship between intraoperative blood loss and the incidence of postoperative complications ( P =0.001, χ²=25). The MSC III subtype was significantly associated with intraoperative blood loss ( P =0.000), length of stay ( P =0.000), and operating time ( P =0.001). Conclusions: Our study purports a strong relationship between greater MSC and complications of all types. As such, surgeons may benefit from preoperative strategies to minimize complications.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.238
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

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

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 head0.094
GPT teacher head0.390
Teacher spread0.296 · 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 teacher head, 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Medicine and SurgerySame topicAdrenal and Paraganglionic TumorsFrench-language works237,207