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Record W7121570717 · doi:10.64483/202522468

Nursing Care and Clinical Considerations in the Management of Suction Drains

2025· article· W7121570717 on OpenAlexaff
Sarah Hussain Kaabi, Shaykhah Rasheed Binmozan, Faten Abdulelah Saad, Maryam Musayeb Assab Al-inaz, Tahani Mudhhi Awadh Alanazi, Noha Mohammad Baiedi, Reem mohammed hadi ameri, Alanood Mawakh Alotaibi, Hani abdullah alawad, Jawaher Aiyedh Almutairi, Norah Hayis Naif almutairi, Abeer Fawaz Alhalfi

Bibliographic record

VenueSaudi Journal of Medicine and Public Health · 2025
Typearticle
Language
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsSuctionNursing careNursing managementWound careNarrative reviewMultidisciplinary approachPatient careNursing assessment

Abstract

fetched live from OpenAlex

Background: Suction drains are widely used in postoperative care to prevent fluid accumulation, support wound healing, and allow early detection of complications. However, their routine use remains controversial due to associated risks and inconsistent evidence of benefit. Aim: This article aims to review the nursing care and clinical considerations involved in the management of suction drains, emphasizing indications, contraindications, techniques, and multidisciplinary roles. Methods: A narrative review approach was used, synthesizing current clinical evidence and nursing practice guidelines related to suction drain use, management, and complications. Results: Suction drains were shown to be effective when used selectively based on surgical and patient-related factors. Proper insertion technique, vigilant monitoring, accurate documentation, and early removal significantly reduced complications such as infection, blockage, and fistula formation. Nursing care was identified as central to safe drain management. Conclusion: Selective use and meticulous nursing management optimize patient outcomes and minimize drain-related risks.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.474
Teacher spread0.367 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueSaudi Journal of Medicine and Public HealthSame topicNosocomial Infections in ICUFrench-language works237,207