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Record W3173787262 · doi:10.1111/iwj.13654

Oncologic feasibility for negative pressure wound therapy application in surgical wounds: A meta‐analysis

2021· review· en· W3173787262 on OpenAlexaboutno aff
Yen‐Jen Wang, Xiaofeng Yao, Yang‐Sheng Lin, Jen‐Yu Wang, Chang‐Cheng Chang

Bibliographic record

VenueInternational Wound Journal · 2021
Typereview
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNegative-pressure wound therapySurgeryMalignancyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Negative pressure wound therapy (NPWT) decreases postoperative complications of various surgeries. However, the use of NPWT for oncological surgical wounds remains controversial. To evaluate the association of NPWT with oncologic recurrence in surgical wounds without residual malignancy, we analysed studies that compared NPWT with conventional non‐pressure dressings for cancer surgical wounds without residual tumour by August 12, 2020. We compared tumour recurrence rates and postoperative complications between the two procedures. The six studies included 118 patients who received NPWT, and 149 patients who received conventional non‐pressure wound care. The overall quality of the included studies was high based on the Newcastle–Ottawa scale score of 7.5. Tumour recurrence after NPWT was not significantly different compared with conventional non‐negative pressure wound care (9.3% versus 11.4%, P = 0.40). There was no significant heterogeneity between the studies (I 2 = 3%). Although NTWT was associated with a lower complication rate compared with the control group, the result was non‐significant ( P = 0.15). Application of NPWT in oncologic resection wounds without residual malignancy revealed no difference in local recurrence and may reduce the risk of postoperative complications compared with conventional non‐negative pressure dressings. NPWT can be considered an alternative method for reconstruction in challenging cases.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.178
GPT teacher head0.468
Teacher spread0.290 · 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.

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".

Quick stats

Citations19
Published2021
Admission routes1
Has abstractyes

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