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Enregistrement W4244427453 · doi:10.1249/mss.0000000000001093

Response

2016· letter· en· W4244427453 sur OpenAlexaffabout
Kristi B. Adamo, Shanna Wilson, Alysha L. J. Dingwall‐Harvey, Kimberly P. Grattan, Patti‐Jean Naylor, Viviene A. Temple, Gary S. Goldfield

Notice bibliographique

RevueMedicine & Science in Sports & Exercise · 2016
Typeletter
Langueen
DomainePsychology
ThématiqueChildren's Physical and Motor Development
Établissements canadiensChildren's Hospital of Eastern OntarioUniversity of VictoriaUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésBonferroni correctionPercentileNull hypothesisPopulationPsychologyGross motor skillStatisticsCognitive psychologyEconometricsMathematicsDemographyMotor skillDevelopmental psychology

Résumé

récupéré en direct d'OpenAlex

Dear Editor-in-Chief, We thank Dr. Robinson and her colleagues for their interest in our work (1) and giving us the opportunity to respond to their letter. The pattern of results with fundamental movement skills was not different using standardized scores, or percentiles, as demonstrated in Table 2. We used percentiles as described in the Test of Gross Motor Development-2 analysis manual, not only because they allow comparisons between groups in change but also change relative to an age-matched population. With regard to adjustment for baseline values, these were not made because there were no significant baseline differences in mean gross motor quotient percentiles (p. 931) and, we used mixed models that adequately account for baseline differences. As for not adjusting for multiple analyses (e.g., Bonferroni), this decision was informed by Rothman (3) a leading epidemiologist and methodologist. “The theoretical basis for advocating a routine adjustment for multiple comparisons is the ‘universal null hypothesis’ that ‘chance’ serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature.” Similar to the pattern of results for standardized scores and percentile scores on the global fundamental movement skills indicators (i.e., GMQ, locomotor skills, object control skills), there were no differences in pattern of results when relative group differences were compared with raw scores versus percentile scores, on the individual locomotor skills and object control skills. Wanting to identify what set of skills might be driving the differences between groups, we chose to examine and present raw scores at both baseline and 6 months in Figures 3A and 3B. We hoped to reflect absolute performance for ease of comparison across studies, while still illustrating group differences at each time point. Visually comparing the unprocessed raw scores (Figs. 3A and 3B), with age-matched and sex-matched standardized scores (determined using tables of normative data (non-Canadian) reported in Table 2, is not appropriate. We did not intend to imply this and apologize for any confusion our presentation of data might have caused. Albeit normative data are not available for Canadian children as indicated, the TGMD-2 scoring guide indicates the GMQ to be the “best measure of an individual’s gross motor ability” and since both intervention and control groups are being compared to these percentiles, we believe our relative group differences are still meaningful. We agree with the need for more consistency in the TGMD score reporting and are hopeful that the updated tool and scoring rubric is more specific and directive. We agree with the suggestion to follow the CONSORT statement and we believe our study was conducted in line with the standards in the statement, as reflected in the primary article from this study (2), referenced in our methods. Given word limitations for MSSE and a request to condense the primary article, we were forced to include only the most pertinent information. Kristi B. Adamo School of Human Kinetics Healthy Active Living and Obesity Research Group Children’s Hospital of Eastern Ontario Research Institute Pediatrics, Faculty of Medicine University of Ottawa Ottawa, CANADA Shanna Wilson Healthy Active Living and Obesity Research Group Children’s Hospital of Eastern Ontario Research Institute Ottawa, CANADA Alysha L. J. Harvey Kimberly P. Grattan School of Human Kinetics University of Ottawa Ottawa, CANADA Patti-Jean Naylor School of Exercise Sciences, Physical & Health Education University of Victoria Victoria, CANADA Viviene A. Temple School of Exercise Sciences, Physical & Health University of Victoria Victoria, CANADA Gary S. Goldfield Healthy Active Living and Obesity Research Group Children’s Hospital of Eastern Ontario Research Institute Pediatrics, Faculty of Medicine University of Ottawa Ottawa, CANADA

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,038
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,888
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,038
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0030,003
Science ouverte0,0030,003
Intégrité de la recherche0,0140,015
Charge utile insuffisante (le modèle a refusé de juger)0,1120,079

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,295
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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