MétaCan
Menu
Back to cohort
Record W2751468669 · doi:10.1016/j.ctcp.2017.08.003

Comparison of pediatric self reports and parent proxy reports utilizing PROMIS: Results from a chiropractic practice-based research network

2017· article· en· W2751468669 on OpenAlexaff
Joel Alcantara, Jeanne Ohm

Bibliographic record

VenueComplementary Therapies in Clinical Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsChiropracticMedicineProxy (statistics)Pediatric researchAlternative medicineFamily medicinePhysical therapyMedical physicsPediatricsPathologyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

OBJECTIVE: To measure the cross-informant variant of pediatric quality of life (QoL) based on self-reports and parent proxy measures. METHODS: A secondary analysis of baseline data obtained from two independent studies measuring the QoL based on the pediatric PROMIS-25 self-report and the PROMIS parent-proxy items banks. A scoring manual associated raw scores to a T score metric (mean = 50; SD = 10). Reliability of QoL ratings utilized the ICC while comparison of mean T Scores utilized the unpaired t-test. RESULTS: A total of 289 parent-child dyads comprised our study responders. Average age for parents and children was 41.27 years and 12.52 years, respectively. The mean T score (child self-report: parent proxy) for each QoL domains were: mobility (50.82:52.58), anxiety (46.73:44.21), depression (45.18:43.60), fatigue (45.59:43.92), peer-relationships (52.15:52.88) and pain interference (47.47:44.80). CONCLUSION: Parents tend to over-estimate their child's QoL based on measures of anxiety, depression, fatigue, peer-relationships and pain interference.

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)0.010
metaresearch head score (Gemma)0.032
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 score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.373
GPT teacher head0.552
Teacher spread0.179 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueComplementary Therapies in Clinical PracticeSame topicPediatric Pain Management TechniquesFrench-language works237,207