Self-Ear Cleaning Practices and the Associated Risks: A Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Naturally the ear produces soft wax from the sebaceous and ceruminous gland. This is what is referred to as earwax. This wax is important for protection of the ear by trapping dust and other foreign particles that could damage the eardrum. It also has some antibacterial properties. Jaw movements, like during chewing, moves the old earwax from inside the ear canal to the outside and finally flakes off. Build-up of this wax in the ear causes hearing loss, pain in the ear, irritation, dizziness and ringing in the ears. Self-ear cleaning refers to self-insertion of objects into the ear canal, with an attempt to remove the wax to get rid of these symptoms. It is a common practice amongst many individuals. Potentially, this rids the ear of its protective defences in addition to posing a risk of ear related injuries. This review paper aims to determine the prevalence of self-ear cleaning, the common methods used and the complications associated with this practice. METHODS: Electronic retrieval of articles for review was done from PubMed, Google and Google scholar with key-ward – self-ear cleaning, ear-wax, cerumen. Many articles were retrieved but only a few were about self-ear cleaning and only seven could be included in this review. The inclusion criteria included: article published in English language; study carried between 2005 and 2020 inclusive; article discussing materials used and complications associated with self-ear cleaning. Articles older than 15 years or published in languages other than English were excluded. RESULTS: On average the prevalence of self-ear cleaning amongst all studies was 76.6%. The commonest method used for ear cleaning was cotton buds with an average of 69.6%. Wax/dirt removal was the commonest reason for engaging in this practice. Several complications arising from this practice included perforation of eardrum, retained foreign body and otitis externa. CONCLUSION: In addition to ridding the ear of its natural protection, self-ear cleaning is associated with a risk of injury to the ear drum and retention of foreign bodies. Community education to avoid this practice is therefore of paramount importance. Trained health care providers should be consulted whenever someone has a problem related to hearing or any other symptoms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,012 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».