Evidence map›Paper›PMID 39018292›Full record

ArticlePloS one2024

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

Kun Lu, Qiang Li, Chun Pu, Xue Lei, Qiang Fu

Abstract read
In one paragraph

Article in PloS one, 2024. 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.

Kun LuDepartment of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.
Qiang LiDepartment of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.
Chun PuDepartment of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.
Xue LeiDepartment of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.
Qiang FuDepartment of Anesthesiology, The Third People's Hospital of Chengdu, Southwest Jiao Tong University, Chengdu, China.ORCID 0009-0004-8504-3292

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and September 2021 were enrolled. According to the preoperative anesthesia management methodology, patients were rolled into a control (Ctrl) group (using 10 mg dexamethasone 1-2 hours before surgery) and an experimental (Exp) group (using a deep learning-based anesthesia monitoring system on the basis of the Ctrl group), with 40 cases in each group. A comprehensive comparative analysis was performed between the two cohorts, encompassing postoperative cognitive evaluations, Montreal Cognitive Assessment (MoCA) scores, gastrointestinal functionality, serum biomarkers (including interleukin (IL)-6, C-reactive protein (CRP), and cortisol levels), length of hospitalization, incidence of complications, and other pertinent metrics. The findings demonstrated that anesthesia monitoring facilitated by deep learning algorithms effectively assessed the anesthesia state of patients. Compared to the Ctrl group, patients in the Exp group showed significant differences in cognitive assessments (word recall, number connection, number coding) (P<0.05). Additionally, the Exp group exhibited a notably increased MoCA score (25.3±2.4), significantly shorter time to first flatus postoperatively (35.8±13.7 hours), markedly reduced postoperative pain scores, significantly shortened time to tolerate a liquid diet postoperatively (19.6±5.2 hours), accelerated recovery of serum-related indicators, and a significantly decreased mean length of hospital stay (11.4±3.2 days) compared to the Ctrl group. In summary, administering dexamethasone under the anesthesia management of GITs surgery based on gradient boosting decision tree (GBDT) and pharmacokinetics pharmacodynamics (PKPD) models can promote patient recovery, reduce the incidence of postoperative cognitive impairment (POCD), and improve patient prognosis.

Indexed as

Deep LearningDexamethasoneGastrointestinal NeoplasmsAgedAged, 80 and overAnesthesiaCognitionFemaleHumansLength of StayMalePostoperative ComplicationsDexamethasone

Identifiers

PMID39018292
PMCPMC11253962

What OpenQuestion holds

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Registered trials

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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.