Summary of best evidence on prevention of intracranial infection after endoscopic endonasal transsphenoidal pituitary neoplasm resection
Bibliographic record
Abstract
Background: Intracranial infection is one of the most serious complications after pituitary neoplasm resection. However, the quality of the evidence for existing preventive measures varies significantly, and the related content is scattered, and the scope is broad. Nurses lack the specificity and targeted guidance for preventing intracranial infections after endoscopic endonasal transsphenoidal surgery (EETS), and nurses find that evidence necessitates screening and identification during its application, and it is challenging to utilize current tool for guiding clinical practice. Thus, the protocols for preventing intracranial infection after EETS required further refinement. The aim of this study is to summarize the relevant evidence for preventing postoperative intracranial infections after endoscopic endonasal transsphenoidal pituitary neoplasm resection, in order to reduce the incidence of postoperative intracranial infection and provide a reference for clinical medical staff. Methods: We systematically searched a variety of platforms, including British Medical Journal Best Practice, UpToDate, DynaMed, Guidelines International Network, Registered Nurses' Association of Ontario, Scottish Intercollegiate Guidelines Network, Australian Joanna Briggs Institute Evidence based Healthcare Center Database, National Institute for Health and Clinical Excellence, Medlive, Wanfang Data, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Cochrane Library, Embase, PubMed, Web of Science, and Chinese biomedical literature service system (Sinomed) to collect clinical decisions, relevant guidelines, evidence summaries, systematic reviews, and expert consensus documents on the prevention of intracranial infection in this context according to the 6S evidence model. The search included literature published up to December, 2023. Then conduct literature screening and evaluation, extract and summarize relevant evidence on perioperative prevention of intracranial infection after EETS from the selected literature. Two researchers applied the JBI levels of evidence preappraisal system (2014 version) to categorize the included evidence into five levels (level 1a being the highest and level 5c being the lowest). Results: A total of 16 pieces of literature were reviewed, including 6 clinical decision-makings, 2 guidelines, 2 systematic reviews, and 6 expert consensus documents. Ultimately, 24 pieces of best evidence for preventing intracranial infections after EETS for pituitary adenomas were formed, and they will be divided into four categories: multidisciplinary collaboration, preoperative evaluation and informed consent, intraoperative prevention and control, and postoperative observation and prevention. Conclusions: This summarized the best evidence for preventing intracranial infection after endoscopic endonasal transsphenoidal pituitary neoplasms resection. Summary of the best evidence for preventing intracranial infections following EETS plays a critical role in enhancing surgical success, optimizing patient management, fostering multidisciplinary collaboration, advancing research, and improving patient satisfaction. It is recommended that medical staff select and apply the evidence in clinical practice in order to avoid the occurrence of intracranial infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".