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Record W4293062875 · doi:10.1155/2022/5213744

[Retracted] Factors Influencing Cerebrospinal Fluid Leaking following Pituitary Adenoma Transsphenoidal Surgery: A Meta‐Analysis and Comprehensive Review

2022· review· en· W4293062875 on OpenAlexaboutno aff
Jiao Zhang, Jingyun Liu, Liyan Huang

Post-publication record

NatureRetraction
ReasonConcerns/Issues about Peer Review;Investigation by Journal/Publisher;
Date11/22/2022 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueBioMed Research International · 2022
Typereview
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTranssphenoidal surgeryCochrane LibraryPituitary adenomaLeakMeta-analysisCerebrospinal fluid leakMEDLINECerebrospinal fluidComplicationAdenomaPituitary neoplasmSurgeryInternal medicinePituitary glandHormoneChemistry

Abstract

fetched live from OpenAlex

Background . Surgical resection is the main method to treat pituitary adenoma. Cerebrospinal fluid leakage (CSF Leak) is the main complication after transsphenoidal surgery. The impact of postoperative CSF Leak can be predicted in advance, and preventive measures can be taken in time. Clinically, a variety of factors may affect the occurrence of postoperative CSF Leak. In this study, meta‐analysis was used to investigate the risk factors of postoperative CSF Leak as a clinical reference. Methods . The databases PubMed, Medline, Embrase, Cochrane library, CNKI, and CBM were searched for all studies on the risk factors of postoperative CSF Leak. Studies were screened and finally included. The quality of the included studies was assessed by the Newcastle‐Ottawa scale. We used Revman 5.4 software to conduct the pooled effect size of every potential statistically significant factor. Results . 13 articles with a total of 5967 patients with pituitary adenoma and 405 cases of postoperative CSF Leak were finally included, accounting for 6.79%. All of the 13 articles had a quality score > 5, indicating good quality. Meta‐analysis showed that patient age (OR = 0.71, 95% CI (0.41, 1.20), P = 0.20) was not a factor influencing postoperative CSF Leak, while BMI (MD = 2.26, 95% CI (1.31, 3.20), P < 0.00001), tumor size (MD = 1.35, 95% CI (0.22, 2.49), P = 0.02), whether a second operation was performed (OR = 2.20, 95% CI (1.45, 3.33), P = 0.0002), and intraoperative CSF Leak (OR = 8.88, 95% CI (3.64, 21.69), P < 0.00001) were risk factors for postoperative CSF Leak in patients. Discussion . BMI, tumor size, reoperation, and intraoperative CSF Leak are the risk factors of postoperative CSF Leak. However, not all the factors were covered in this study, it is still worth continuing to deeply investigate in this topic.

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)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.015
Bibliometrics0.0070.007
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.314
GPT teacher head0.455
Teacher spread0.142 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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