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Record W2595793628

Clinical curative effect of video-assisted breast surgery by single incision through the anterior axillary line for removing mammary fibroma

2017· article· en· W2595793628 on OpenAlexaboutno aff
Guo Yan-xiang, Wei Li

Bibliographic record

VenueBiomedical Research-tokyo · 2017
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryAreolaComplicationBlood loss
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study aims to investigate the clinical curative effect of Video-Assisted Breast Surgery (VABS) by single incision through the anterior axillary line for removing mammary fibroma. Methods: Sixty-eight mammary fibroma cases treated in the hospital from November 2013 to June 2015 were selected and randomly divided into control and observation groups (34 cases per group). The control group was subjected to conventional ring areola incision, and the observation group was subjected to VABS by single incision through the anterior axillary line. Intraoperative blood loss, incision length, postoperative complication, and cosmetic effects after the operation were determined and compared between the two groups. Results: The observation group showed shorter incision length and hospital stay and less intraoperative blood losses than those in the control group (p 0.05). The complication rates in the observation and control groups were 5.88% and 23.33%, respectively (p<0.05). The Vancouver scar scale score in the observation group was lower than that in the control group. Furthermore, breast appearance satisfaction score in the observation group was significantly higher than that in the control group (p<0.05). Conclusion: Patients with mammary fibroma who underwent VABS received improved clinical curative effects in terms of few intraoperative injuries, fast recovery after the surgery, low complication rate, non-restricted lesion depth limit, and satisfactory cosmetic effect. Overall, this technique improved the prognosis of the patients.

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)0.006
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.120
GPT teacher head0.441
Teacher spread0.322 · 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
Published2017
Admission routes1
Has abstractyes

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