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Record W2018185129 · doi:10.1097/pec.0b013e31803f7566

Guidelines to Practice

2007· article· en· W2018185129 on OpenAlexaff
Savithiri Ratnapalan, Suzan Schneeweiss

Bibliographic record

VenuePediatric Emergency Care · 2007
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Pediatric sedation practices vary among institutions and even within the same institution depending on providers and location. We planned to implement a pediatric procedural sedation program for a tertiary care pediatric emergency department to standardize sedation practices among emergency physicians. METHODS: An interactive contextual planning model was adapted, and several tasks were initiated simultaneously. The director of pediatric emergency medicine and clinical director of the institution approved the proposal for the sedation program. Needs assessment surveys and focus group interviews were conducted to identify educational needs of the target audience and infuse a sense of ownership. A grant was obtained from the institution because the budget exceeded available divisional funds. Other pediatric sedation guidelines and published literature were used to produce a sedation handbook and pocket card. Interim approval was obtained from the Drugs and Therapeutics Committee and the Patient Care Committee. RESULTS: The program was successfully implemented after all physicians and nurses working in the emergency department attended a half-day sedation course and completed a multiple-choice examination. Random chart audits verify that the emergency physicians are performing almost all procedural sedations now as per protocol. CONCLUSIONS: Implementing a structured program facilitates guideline adherence. Adapting a flexible contextual planning model was successful in translating guidelines to practice where resources were limited, and the target audience was highly trained adult learners.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.448
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.030
GPT teacher head0.388
Teacher spread0.358 · 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 designNot applicable
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
Published2007
Admission routes1
Has abstractyes

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