Self-Ear Cleaning Practices and the Associated Risks: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".