Systemic Steroids for Otolaryngology–Head and Neck Surgery Disorders: An Evidence‐Based Primer for Clinicians
Bibliographic record
Abstract
OBJECTIVE: To offer pragmatic, evidence-informed guidance on the use of systemic corticosteroids (SCS) for common otolaryngologic disorders. DATA SOURCES: PubMed, Cochrane Library, and American Academy of Otolaryngology-Head and Neck Surgery Foundation clinical practice guidelines. REVIEW METHODS: A comprehensive search of published literature through November 2021 was conducted on the efficacy of SCS, alone or in combination with other treatments, for managing disorders in otolaryngology and the subdisciplines. Clinical practice guidelines, systematic reviews, and randomized controlled trials, when available, were preferentially retrieved. Interventions and outcomes of SCS use were compiled to generate summary tables and narrative synthesis of findings. CONCLUSIONS: Evidence on the effectiveness of SCS varies widely across otolaryngology disorders. High-level evidence supports SCS use for Bell's palsy, sinonasal polyposis, and lower airway disease. Conversely, evidence is weak or absent for upper respiratory tract infection, eustachian tube dysfunction, benign paroxysmal positional vertigo, adenotonsillar hypertrophy, or nonallergic rhinitis. Evidence is indeterminate for acute laryngitis, acute pharyngitis, acute sinusitis, angioedema, chronic rhinosinusitis without polyps, Ménière's disease, postviral olfactory loss, postoperative nerve paresis/paralysis, facial pain, and sudden sensorineural hearing loss. IMPLICATIONS FOR PRACTICE: Clinicians should bring an evidence-informed lens to SCS prescribing to best counsel patients regarding the risks, anticipated benefits, and limited data on long-term effects. Alternate routes of corticosteroid administration-such as sprays, drops, inhalers, and intralesional injections-may be preferable for many disorders, particularly those that are self-limited or require a prolonged duration of therapy. Prudent use of SCS reduces the risk of medication-related adverse effects. Clinicians who are conversant with high-level evidence can achieve optimal outcomes and stewardship when prescribing SCS.
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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 | 0.067 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".