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Record W3198297573 · doi:10.37897/rjmp.2017.4.3

PROCEDURES IN PLASTIC SURGERY OF SOFT TISSUES. FROM ADIPOSE TISSUE GRAFTS TO STEM CELLS

2017· article· en· W3198297573 on OpenAlexaff
Sorin Nae, Laura Kuhlmann, Ion Bordeianu

Bibliographic record

VenueRomanian Journal of Medical Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsAdipose tissueSoft tissueMedicineSurgeryRejuvenationTransplantationPlastic surgeryReconstructive surgeryStem cellBiologyInternal medicine

Abstract

fetched live from OpenAlex

Although significant progress has been made in recent years in plastic and reconstructive surgery procedures, there are still many issues to be solved. Thus, autologous fat transplantation is one of the promising treatments for soft tissue augmentation and facial rejuvenation, due to the lack of incisional scarring and complications associated with foreign materials. However, unpredictable problems and a low rate of graft survival due to partial necrosis occur. Recent research has led to the development of new adipose tissue transplantation techniques, with promising results, without establishing a standard protocol.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.007

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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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