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Record W2805952772 · doi:10.3390/children5060069

Specialized Care without the Subspecialist: A Value Opportunity for Secondary Care

2018· article· en· W2805952772 on OpenAlexaff
Eyal Cohen, C. Jason Wang, Barry Zuckerman

Bibliographic record

VenueChildren · 2018
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCommonwealth Fund
KeywordsValue (mathematics)MedicinePsychologyComputer science

Abstract

fetched live from OpenAlex

An underutilized value strategy that may reduce unnecessary subspecialty involvement in pediatric healthcare targets the high-quality care of children with common chronic conditions such as obesity, asthma, or attention deficit hyperactivity disorder within primary care settings. In this commentary, we propose that "secondary care", defined as specialized visits delivered by primary care providers, a general pediatrician, or other primary care providers, can obtain the knowledge, skill and, over time, the experience to manage one or more of these common chronic conditions by creating clinical time and space to provide condition-focused care. This care model promotes familiarity, comfort, proximity to home, and leverages the provider's expertise and connections with community-based resources. Evidence is provided to prove that, with multi-disciplinary and subspecialist support, this model of care can improve the quality, decrease the costs, and improve the provider's satisfaction with care.

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0040.011
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0210.021
Insufficient payload (model declined to judge)0.0030.001

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 head0.055
GPT teacher head0.402
Teacher spread0.347 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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