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Record W4407517436 · doi:10.1001/jamaoto.2024.5047

Reduction of Bleeding From Pterygopalatal Injection

2025· review· en· W4407517436 on OpenAlexaboutno aff
Sung Ryul Shim, Jieun Shin, Cheol Mog Hwang, Yong Kyun Kim, Jong Bum Park, Jong‐Yeup Kim

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2025
Typereview
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCochrane LibrarySinusitisLidocaineRandomized controlled trialData extractionMEDLINEMeta-analysisSurgeryEndoscopic sinus surgeryGrading (engineering)Functional endoscopic sinus surgeryAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Importance: Endoscopic sinus surgery (ESS) is a minimally invasive surgical method that is widely used in the treatment of various sinonasal conditions, including chronic sinusitis, nasal polyps, and fungal sinusitis. However, intraoperative bleeding remains a significant challenge. Objective: To evaluate the effects of pterygopalatal injections with lidocaine and adrenaline during sinus surgery. Data Sources: PubMed/MEDLINE, the Cochrane Library, and EMBASE were systematically searched from database inception through July 31, 2024. Two authors also manually and independently searched all relevant studies. Study Selection: Randomized clinical trials with (1) patients diagnosed with sinusitis; (2) interventions that included pterygopalatal injections with lidocaine and adrenaline; (3) comparisons that were specified as normal saline or no injection; and (4) outcomes that used subjective scores (Boezaart surgical field grading [BSFG]) and objective markers (amount of bleeding, duration of surgery, and mean arterial pressure [MAP]). Data Extraction and Synthesis: Data extraction was completed independently by 2 extractors and cross-checked for research integrity. The pairwise meta-analysis was performed to compare the treatment group with control used in ESS. Hedges g standardized mean differences (SMDs) and mean differences (MDs) were used for improvement in all outcomes. Main Outcomes and Measures: Efficacy outcomes included subjective scores, such as BSFG, as well as objective markers measuring amount of bleeding, duration of surgery, and MAP. Results: A comprehensive literature search identified 322 patients from 7 studies. The studies were conducted in Australia, Canada, Egypt, India, and Iran. The mean age range was 30 to 48.8 years, and 36.7% to 66.7% of the study populations were male. In most studies, the observation time of BSFG was measured at 15-minute intervals and measured from a minimum of 15 minutes to a maximum of 150 minutes. The pooled SMD for BSFG between treatments vs the control group was -1.01 (95% CI, -1.72 to -0.30), indicating that pterygopalatal injection with lidocaine and adrenaline was associated with improvement in the surgical field condition. The pooled MD for MAP between treatments vs the control group was -0.49 mm Hg (95% CI, -0.91 to -0.07), indicating that pterygopalatal injection was associated with significantly reduced MAP. The pooled MD for amount of bleeding between treatments vs the control group was -9.47 mL (95% CI, -29.05 to 10.11), and the pooled MD for duration of surgery between treatments vs the control group was -4.28 minutes (95% CI, -12.85 to 4.29), indicating that that this technique was not significantly associated with amount of bleeding or duration of surgery. Conclusions and Relevance: The findings of this systematic review and meta-analysis indicate that pterygopalatal injection can be an effective method for reducing BSFG during ESS to improve the surgical field. This can be achieved by significantly improving the surgical field of view and lowering MAP.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.043
GPT teacher head0.326
Teacher spread0.283 · 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 designNot applicable
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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