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Record W4391754538 · doi:10.1097/icl.0000000000001070

Double-Blind Comparison of Eyelash Epilation Techniques for the Purpose of Demodex folliculorum Mite Retrieval

2024· article· en· W4391754538 on OpenAlexaff
Etty Bitton, Walter Wittich

Bibliographic record

VenueEye & Contact Lens Science & Clinical Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsAssociation for Canadian StudiesUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsEyelashDemodex folliculorumDemodexMiteDandruffDermatologyMedicineBiologyShampooPathologyBotany

Abstract

fetched live from OpenAlex

OBJECTIVE: Demodex folliculorum blepharitis is typically confirmed with lash epilation and microscopic identification of mites. However, mite counts may vary with the epilation technique. As there is no gold standard to epilating lashes for the purposes of mite counts, the aim of this study was to compare three epilation techniques. METHOD: A prospective randomized double-blind study compared three epilation techniques on lashes with cylindrical dandruff. Techniques included (A) direct pulling of the lash; (B) rotating the lash before epilation; and (C) sliding the cylindrical dandruff away, lash rotation, and epilation. Mean mite counts were analyzed using a repeated-measures analysis of variance. RESULTS: Forty (n=40) participants (20 M: 20 F, mean age of 62.3±17.1 years) revealed similar mite counts between right (1.43±1.74) and left (1.35±1.59) eyes ( P =0.63). A significant difference ( P =0.03) in mite count was noted (technique A: 1.05 ± 1.60; technique B 1.76 ± 1.80; and technique C 1.36 ± 1.54) with technique B yielding the highest mite count ( P =0.04). CONCLUSION: Demodex mite count is a key parameter in establishing infestation or to determine treatment efficacy. This study revealed that rotating the lash before epilation yielded the highest mite count. Future studies should report the epilation technique used to allow for study comparisons.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.143
GPT teacher head0.509
Teacher spread0.365 · 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 designNot applicable
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

Citations4
Published2024
Admission routes1
Has abstractyes

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