Evidence map›Paper›PMID 40931462›Full record

ArticleInternational journal of cosmetic science2026

Cyclic combing of untreated and bleached human hair: Analysis of the time-dependent breakage of hair through recording the formation of fibre fragments.

Thomas Davies, Gabriele Wortmann, Franz J Wortmann

Abstract read
In one paragraph

Article in International journal of cosmetic science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Thomas DaviesBossa Nova Vision/Dia-Stron Inc, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-2027-6237
Gabriele WortmannDepartment of Materials, School of Natural Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-6953-9563
Franz J WortmannDepartment of Materials, School of Natural Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-0076-9552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMachine-based cyclic combing of hair tresses under dry conditions is a proven method for evaluating hair strength and the impact of treatments. Recent advancements in image analysis allow for a detailed review of hair fragment lengths and quantities produced after specific combing cycles. Our aim is to provide an in-depth analysis of the kinetics of hair fragment formation.

methodsWe analysed the combing performance of untreated and bleached European straight hair, assessing two conditioning treatments. Hair tresses underwent 5000 combing cycles, with the increasing number of fragments recorded. Results were fitted using a three-parameter Voigt-Kelvin model.

resultsThe fragment counts were log-normally distributed in all cases. The model uses ln(N

conclusionsThe selected function's characteristics and the invariance of FCI indicate that friction primarily controls fragment formation in straight hair during combing. This suggest that there is no direct link between combing performance and tensile fatigue failure for this hair type. In contrast, textured hair will likely show more complex combing performance. Our analysis shows that the total number of fragments after many combing cycles can predict early failures and assess conditioning agents, thereby supporting product claims about 'hair strength'.

Indexed as

Beauty CultureHairHumansclaim substantiation for hair ‘strength’combingfibre breakagehair treatmentkinetic analysis of fragment formationstatistics

Identifiers

PMID40931462
PMCPMC13068040

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.