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Record W4412137669 · doi:10.1093/jscr/rjaf449

Efficacy of negative pressure wound therapy blowhole placement in alleviating severe subcutaneous emphysema and associated transient blindness following video-assisted thorascopic surgery: a case series

2025· article· en· W4412137669 on OpenAlexaff
Yingtong Gao, Elisabeth Savonitto, Shuyin Liang, Alison Wallace

Bibliographic record

VenueJournal of Surgical Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPneumothorax, Barotrauma, Emphysema
Canadian institutionsQueen Elizabeth II Health Sciences CentreLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineSurgeryBlindnessNegative-pressure wound therapySubcutaneous emphysemaAnesthesiaPathologyComplication

Abstract

fetched live from OpenAlex

Video-assisted thorascopic surgery (VATS) lobectomy is widely used for treating lung cancer, but severe subcutaneous emphysema can be a rare complication, leading to distressing symptoms such as pain, temporary blindness, and voice changes. Traditional management strategies often require prolonged treatment, and blowhole incisions can result in infection and discomfort. This case series explores the use of negative pressure wound therapy (NPWT) applied via a unilateral blowhole incision to treat severe subcutaneous emphysema post-VATS lobectomy. In three cases, NPWT facilitated rapid resolution of pain, restored vision, and resolved voice changes within 8 h. No infection-related complications occurred. This approach not only accelerates recovery and alleviates patient discomfort but also reduces the burden on caregivers and healthcare teams. NPWT should be considered a valuable addition to the management of severe subcutaneous emphysema, improving patient outcomes and enhancing postoperative care.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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 designCase report
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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