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Record W2154927426 · doi:10.12966/hc.5.4.2013

Hypertrophic Scar and Pregnancy

2013· article· en· W2154927426 on OpenAlexaboutno aff
Shuchi Jain, Madhu Jain, Vaibhav Jain, Pradeep Jain

Bibliographic record

VenueHealth care · 2013
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic scarPregnancyObstetricsMedicineSurgeryBiology

Abstract

fetched live from OpenAlex

Context: Prolonged wound inflammation predisposes to hypertrophic scar formation. Scar growth may also be stimulated by various hormones which might be responsible for higher incidence of keloid formation during pregnancy and decrease in its size after the menopause. Aim: To find out a suitable anti-scar treatment to suit every patient even in pregnancy. Setting: Pregnant patients have a special situation where every drug can not be administered due to Teratogenic potential and co-existing physiological levels of discomfort such as abdominal distension so that established treatment protocols like intralesional steroid and pressure garments may not be found suitable. Methods and design: It is a prospective study. We hereby present a report of two cases, one with a woman in late pregnancy sustaining burn and another with a pre-existing scar getting hypertrophied during pregnancy. Out of several treatment modalities, we preferred to use an herbal cream which had been found to be safe and innocuous in earlier study carried out by one of the authors with good result. The cream was applied twice a day over the scar and massaged gently till it disappeared. The scars were evaluated on Vancouver Scar Scale. Result: Troublesome itching remarkably decreased by 2 weeks of continuous application. All the parameters showed appreciable improvement in the hypertrophic scars associated with pregnancy by three months. There was no side effect with the use of herbal cream.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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 designObservational
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

Citations10
Published2013
Admission routes1
Has abstractyes

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