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Record W2917308752 · doi:10.2147/ahmt.s199489

<p>The creation of a national coalition to target adolescent idiopathic scoliosis: a meeting report</p>

2019· article· en· W2917308752 on OpenAlexaffabout
Milena Cioana, Devin Peterson, Paul Missiuna, Ron El‐Hawary, Timothy P. Carey, Murray Potter, Laura Banfield, Lehana Thabane, M. Constantine Samaan

Bibliographic record

VenueAdolescent Health Medicine and Therapeutics · 2019
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsImpactSt. Joseph’s Healthcare HamiltonWestern UniversitySt Joseph's Health CareDalhousie UniversityMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsIdiopathic scoliosisScoliosisPsychologyMedicinePolitical sciencePhysical medicine and rehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

In this report, we document the discussions and recommendations of a national conference designed to create a coalition to tackle adolescent idiopathic scoliosis (AIS) held on June 6 and 7, 2017 in Hamilton, ON, Canada. The goal of the establishment of this coalition is to join the efforts of patients, parents, physicians, researchers and other stakeholders to identify stakeholders' perspectives and to categorize gaps in knowledge and target further AIS research and clinical care priorities. The participants' main priorities included focus on shared decision making regarding clinical and research priorities between the stakeholders on the clinical, research and policy sides with patients and families. In addition, improvements in the dissemination of information via digital platforms and identification of cost-effective screening strategies that may help early identification and intervention were also recognized as a priority. Commitment was reached to form a national coalition to understand the determinants of this condition and enhance patient outcomes through improved clinical care and research efforts.

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)0.002
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 score0.252
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.357
Teacher spread0.304 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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