Evidence-Based Algorithm for the Evaluation of a Child with Bilateral Sensorineural Hearing Loss
Bibliographic record
Abstract
OBJECTIVE: To develop an evidence-based algorithm for determining the etiology of bilateral sensorineural hearing loss (SNHL) in a child. METHODS: The frequency of different etiologies was previously determined. A systematic review of the literature for articles published between 1940 and January 2003 was performed for studies providing information on the diagnosis of each etiology relevant to their clinical presentation. RESULTS: Connexin mutation testing is highly sensitive and specific. CT scanning of the temporal bones is frequently valuable in detecting inner ear malformations. Routine laboratory studies are rarely helpful. ECG is particularly valuable when a history of syncope or arrhythmias or a family history of sudden death in a young child is elicited. There is no literature to support routine urinalysis for the diagnosis of Alport syndrome and thyroid studies lack specificity in the absence of physical findings (goiter). CONCLUSIONS: An evidence-based algorithm was developed that included: history, physical and audiological evaluation, and ophthalmological evaluation. Further directed investigations may include genetic testing for the Cx26 mutation, CT scan of the temporal bones, ECG and urinalysis.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".