MétaCan
Menu
Back to cohort

A weight‐based formula for tracheal tube size in children

2009· article· en· W1973671009 on OpenAlexaff
Naveen Eipe, Nicholas Barrowman, Hilary Writer, Dermot R. Doherty

Bibliographic record

VenuePediatric Anesthesia · 2009
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineCohortSignificant differenceAnimal scienceNuclear medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Age (in years) of the child has conventionally been used in formulae to estimate the tracheal tube (TT) size. The objective of this retrospective study was to test a weight-based formula (WBF) for uncuffed oral TT in children and compare it with the conventional age-based formula (ABF). METHODS: The patient's age, weight, and size of TT internal diameter (ID) were recorded. For comparative analysis, the actual TT size used was compared with predicted TT size, calculated using both the standard ABF [ID = age (years)/4 + 4 mm] and the WBF [ID = weight (kg)/10 + 3.5 mm]. RESULTS: The Pearson's correlation coefficient for age and actual TT size used was 0.77 (95% CI: 0.74-0.80) and between weight and actual TT used was 0.70 (95% CI: 0.66-0.74). The ABF correctly predicted 51.3% of TT sizes while the WBF correctly predicted 44.8% of TT sizes (P = 0.01). The measures of agreement between the actual and predicted TT size were 0.35 and 0.27 for the ABF and WBF respectively. The difference between the percentages of paired predictions for the ABF and WBF was statistically significant (P < 0.001) suggesting that, when correctly predicting the actual tube size used, the WBF functions for a different subset of the patient cohort than the ABF. CONCLUSIONS: This study suggests that in this patient cohort, the WBF is statistically inferior to the conventional ABF. However, our findings also suggest that the WBF may correctly predict TT sizes in a subset of patients in whom the ABF is inaccurate.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.655

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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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 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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuePediatric AnesthesiaSame topicTracheal and airway disordersFrench-language works237,207