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Record W4401529739 · doi:10.5863/1551-6776-29.4.375

Identification of a Conversion Factor for Dexmedetomidine to Clonidine Transitions

2024· article· en· W4401529739 on OpenAlexaff
Jasmine Stroeder, Deonne Dersch‐Mills

Bibliographic record

VenueThe Journal of Pediatric Pharmacology and Therapeutics · 2024
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsDexmedetomidineClonidineMedicineSedationAnesthesiaIntensive care unitIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine a conversion factor for use when switching from dexmedetomidine infusion to enteral clonidine in critically ill neonates. METHODS: This was an observational, retrospective review of conversions from dexmedetomidine to -clonidine, performed in a neonatal intensive care unit (NICU) between January 2020 and December 2021. Both initial conversion factors and those resulting after a 48-hour titration period were examined. Sedation and withdrawal scores were measured, and doses were titrated based on a standardized practice within the unit. RESULTS: A total of 43 dexmedetomidine to clonidine conversions were included. The median (IQR) dexmedetomidine dose prior to conversion was 17.4 (11.3-24.0) mcg/kg/day (0.7 mcg/kg/hr) and the median (IQR) enteral clonidine dose post titration was 7.8 (4.7-9.3) mcg/kg/day (2 mcg/kg every 6 hours). This equated to a post-titration conversion factor of approximately 0.42. All neonates had also received opioid infusions while on dexmedetomidine and 60% were on concurrent opioids at the time of the clonidine conversion. CONCLUSIONS: Neonatal clinicians may find the conversion factor identified in this study a useful starting point when converting from dexmedetomidine infusion to enteral clonidine in practice and should be -reminded of the most important steps in conversions (monitoring and follow-up) owing to the variability in this patient group. More studies are needed to elucidate the impact of patient-specific factors on this -conversion process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.362
Teacher spread0.320 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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