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Record W4380606805 · doi:10.1166/mex.2023.2238

The dose-effect regularity of artificial dermis combined with growth factor in repair wound of luxation of the bone tendon

2023· article· en· W4380606805 on OpenAlexaboutno aff
Fengli Ren, Xiaodi Yang, Zhiming Xin, Chengdong Wang, Zhao Liu

Bibliographic record

VenueMaterials Express · 2023
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDermisMedicineSurgeryTendonConfidence intervalGrowth factorAnatomyInternal medicine

Abstract

fetched live from OpenAlex

To investigate the treatment efficacy of the artificial dermis combined with growth factor surgery in patients with luxation of bone tendon (LBT). A total of 40 patients with LBT in our prospective clinical research are randomly allocated to following four groups: Control, low dose, medium dose, and high dose. The baseline characteristics, skin graft interval time, overage rate of regenerated tissue at bone and tendon, and visual estimation of patients were measured, which were then utilized to assess the treatment efficacy of the artificial dermis combined with growth factor surgery in LBT patients. Our outcomes indicated that the artificial dermis combined with growth factor surgery showed significantly less skin graft interval time, higher overage rate of regenerated tissue at bone and tendon, less Vancouver scar scale score compared to those treated by conventional imaging technology (all P < 0.05). It concludes that the artificial dermis combined with growth factor surgery can improve the treatment efficacy, and the medium dose growth factor is a promise dose to treat the patients with LBT.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

Explore more

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