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Record W2396038900 · doi:10.1597/15-292

A Standard Set of Outcome Measures for the Comprehensive Appraisal of Cleft Care

2016· article· en· W2396038900 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHospital for Sick Children
FundersNational Institute of Dental and Craniofacial Research
KeywordsOutcome (game theory)Set (abstract data type)Standard of carePsychologyComputer scienceMedicineMathematicsSurgeryMathematical economicsProgramming language

Abstract

fetched live from OpenAlex

Care of the patient with cleft lip and/or palate remains complex. Prior attempts at aggregating data to study the effectiveness of specific interventions or overall treatment protocols have been hindered by a lack of data standards. There exists a critical need to better define the outcomes-particularly those that matter most to patients and their families-and to standardize the methods by which these outcomes will be measured. This report summarizes the recommendations of an international, multidisciplinary working group with regard to which outcomes a typical cleft team could track, how those outcomes could be measured and recorded, and what strategies may be employed to sustainably implement a system for prospective data collection. It is only by agreeing on a common, standard set of outcome measures for the comprehensive appraisal of cleft care that intercenter comparisons can become possible. This is important for quality-improvement endeavors, comparative effectiveness research, and value-based health-care reform.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.045
GPT teacher head0.348
Teacher spread0.303 · 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