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Pediatric cardiac intensivists: Are enough being trained?*

2004· article· en· W2082028237 on OpenAlexaboutno aff
Daniel Stromberg

Bibliographic record

VenuePediatric Critical Care Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIntensivistMedicineCritically illIntensive careIntensive care unitGraduation (instrument)Pediatric intensive care unitFamily medicineEmergency medicineMedical emergencyIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine both the number of cardiac intensivists being trained by member institutions of the Pediatric Cardiac Intensive Care Society and the perceived need for these professionals. DESIGN: Web-based survey of pediatric cardiac intensive care unit program directors. RESULTS: A total of 54 directors completed the survey (41% response rate). Twelve pediatric cardiac critical care fellowship positions are offered each year among the responding Pediatric Cardiac Intensive Care Society institutions in the United States and Canada-only six of the 12 positions were filled in the academic year 2002-2003. Cardiac intensivist recruitment was ongoing at 25 of the programs surveyed (46%). An additional 45 cardiac intensivists will be sought during the next 1-3 yrs and 36 during the subsequent 3- to 5-yr period. CONCLUSIONS: There is a discrepancy between the current and growing need for trained pediatric cardiac intensivists and the graduation rate of these professionals from teaching programs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.031
GPT teacher head0.360
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2004
Admission routes1
Has abstractyes

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