Evidence map›Paper›PMID 41399545›Full record

ArticleCureus2025

Improving CT Sinus Reporting for Endoscopic Sinus Surgery Using the CLOSE Criteria: A Quality Improvement Project.

Teck Yon Lee, Hamish Iyer, Ashutosh Ranade, Richard Steven, Sophia Wong Ching Hwai

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Teck Yon LeeGeneral Surgery, NHS Fife, Kirkcaldy, GBR.
Hamish IyerGeriatrics, Victoria Hospital, Kirkcaldy, GBR.
Ashutosh RanadeRadiology, Victoria Hospital, Kirkcaldy, GBR.
Richard StevenOtolaryngology - Head and Neck Surgery, Victoria Hospital, Kirkcaldy, GBR.
Sophia Wong Ching HwaiRespiratory Medicine, Northern Care Alliance NHS Foundation Trust, Greater Manchester, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Pre-operative CT sinus reports are essential for identifying high-risk anatomical variants during functional endoscopic sinus surgery (FESS). The widely accepted CLOSE criteria provide a structured framework for this reporting. The objective of this quality improvement (QI) project was to assess the completeness of CT sinus reporting for patients likely to undergo FESS using the CLOSE framework and introduce interventions to standardise reporting and improve patient safety. Methods This three-cycle QI project was conducted at a single NHS health board, reviewing 92 retrospective CT sinus reports between February 2025 and September 2025. Educational interventions were implemented following each cycle, including the distribution of a poster to relevant radiology departments and an NHS-partnered teleradiology company, the collection of feedback, and the reinforcement of changes at departmental meetings. An accompanying survey was sent out to ENT consultants to gather qualitative data. Compliance rates between cycle one and cycle three were compared using the chi-square test. Results Comparing cycle one to cycle three, a significant increase in compliance was observed. Full documentation of all CLOSE components rose from a baseline of 8/51 (15.7%) to 11/21 (52.4%). For reports demonstrating high-risk anatomical variants relevant to surgical planning, overall compliance rose from 5/51 (9.8%) to 9/21 (42.9%) (χ

Indexed as

ct sinusfunctional endoscopic sinus surgery (fess)neuroradiologyquality improvement projectsradiology templatesinonasal anatomical variantssinus diseasestructured reporting template

Identifiers

PMID41399545
PMCPMC12702529

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.