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Record W3004509274 · doi:10.3390/nu12020438

The Cow’s Milk-Related Symptom Score (CoMiSSTM): Health Care Professional and Parent and Day-to-Day Variability

2020· article· en· W3004509274 on OpenAlexaff
Yvan Vandenplas, Eva Carvajal, Stefaan Peeters, Nadine Balduck, Yesra Jaddioui, Carmen Ribes‐Koninckx, Koen Huysentruyt

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsDay careMedicineDay to dayAnimal sciencePsychologyBiologyNursing

Abstract

fetched live from OpenAlex

The Cow’s Milk-related Symptom Score (CoMiSSTM) was created as an awareness tool for cow’s milk allergy. The aim of the present study was to analyze the inter-rater variability between a pediatrician, parents, and day to day variability. A Health Care Professional (HCP) and parent filled in the CoMiSS independently and blinded for each other to evaluate inter-rater variability. In order to validate day-to-day variability, a parent filled in the CoMiSS during 3 consecutive days and was compared to the CoMiSS scored by the HCP. The absolute agreement between parent and HCP was 75%, and 92.6% and 100% with a tolerance of 0, 1, and 2 points, respectively, resulting in excellent agreement with an intraclass correlation coefficient (ICC) 0.981 (95% Confidence Interval 0.974–0.986, p < 0.001). Day-to-day variability during 3 consecutive days resulted in an absolute agreement of 30%, increasing to 80% and 88.6% when 2 and 3 points, respectively, were accepted. The ICC was excellent for the parental prospective scores (0.93, 95% CI 0.90–0.96; p < 0.001). Day-to-day variability indicates that CoMiSS has a moderate inter-rater reliability. A very low variability was observed when scored prospectively over three days. Data suggest that the CoMiSS can reliably be scored by parents without additional training.

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)0.000
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 score0.076
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.025
GPT teacher head0.307
Teacher spread0.281 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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