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Ototoxic drugs and sensorineural hearing loss following severe neonatal respiratory failure

2006· article· en· W2614234924 on OpenAlexaffabout
Charlene M.T. Robertson, M. Tyebkhan Juzer, Abraham Peliowski, Cynthia Philip, Po‐Yin Cheung

Bibliographic record

VenueActa Paediatrica · 2006
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsRoyal Alexandra HospitalGlenrose Rehabilitation HospitalUniversity of AlbertaCapital District Health Authority
Fundersnot available
KeywordsMedicineSensorineural hearing lossHearing lossOtotoxicityVancomycinRespiratory failurePediatricsAnesthesiaInternal medicineAudiologyChemotherapyStaphylococcus aureus

Abstract

fetched live from OpenAlex

Abstract Aim: To determine relationships between ototoxic drugs and 4‐y sensorineural hearing loss (SNHL) in near‐term and term survivors of severe neonatal respiratory failure. Methods: All 81 survivors of the Canadian arm of the Neonatal Inhaled Nitric Oxide Study (mortality 32, loss to follow‐up 9) received loop diuretics, aminoglycosides, and neuromuscular blockers (NMB), and 50 received vancomycin as neonates. Prospective, longitudinal secondary outcome using audiological tests diagnosed late‐onset, progressive SNHL in 43 (53%); not flat (sloping) in 29, flat (severe to profound) in 14. Risk for SNHL was determined. Results: A combination of duration of diuretic use of >14 d and average NMB dose of >0.96 mg/kg/d contributed to SNHL among survivors (odds ratio 5.2; 95% CI 1.6, 16.7). Markers of illness severity did not contribute. Dosage or duration of aminoglycosides use did not relate to SNHL. Cumulative dosages and duration of use of diuretics; NMB; use of vancomycin; and overlap of diuretics with NMB, aminoglycosides, and vancomycin individually linked to SNHL ( p <0.001). Conclusion: Overuse of loop diuretics and/or NMB contributes to SNHL after neonatal respiratory failure; markers of illness severity or the appropriate administration of aminoglycosides do not.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.302
Teacher spread0.283 · 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.

Study designObservational
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

Citations13
Published2006
Admission routes2
Has abstractyes

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