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Record W3147500895 · doi:10.52336/acm.2020.9.6.01

Practical application of kinesiotaping in the case of a cesarean section scar

2020· article· en· W3147500895 on OpenAlexaff
Iga Daniszewska-Jarząb, Sławomir Jarząb

Bibliographic record

VenueAesthetic Cosmetology and Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsScarsMedicineHypertrophic scarsLymphatic systemScar tissueLymphatic tissuesCaesarean sectionMicrocirculationMuscle toneSurgeryPathologyRadiologyPregnancyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

A scar is a skin change resulting from the healing process following, among others, a mechanical injury. Its reconstruction is an important stage that determines the final appearance of the scar and the functionality of the adjacent tissues. The aim of the study is to present the possibility of kinesiotaping in reducing the undesirable effects of a postoperative scar after a C-section on tissue mobility and disturbance of muscle tone. Kinesiotaping supports the spontaneous healing of the scar by reducing pain sensations, activating the lymphatic system, microcirculation, improving the functioning of the surrounding muscles and joints and normalizing muscle tension. Taping the scars is carried out using various techniques, where the direction of sticking or the extent to which the tape is stretched is important. The following techniques are useful in the treatment of caesarean scars: the Z technique, the step technique or the isolated fascial technique.

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)0.001
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.043
GPT teacher head0.337
Teacher spread0.294 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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