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Record W2027826823 · doi:10.1097/htr.0b013e3181b97a4e

Using Activity Diaries to Measure Children's and Adolescents' Compliance With Activity Restrictions After Mild Traumatic Brain Injury

2009· article· en· W2027826823 on OpenAlexaff
Isabelle Gagnon, Bonnie Swaine, Robert Forget

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTraumatic brain injuryMedicinePhysical activityTrauma centerInjury preventionEnergy expenditureCompliance (psychology)Poison controlPhysical therapyPediatricsPsychologyEmergency medicinePsychiatryRetrospective cohort studySurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To examine the extent to which children and adolescents with a mild traumatic brain injury (MTBI) comply with the activity restrictions received upon discharge from a pediatric trauma center. PARTICIPANTS: Thirty-four children with MTBI (aged 8-16 years) and 34 control children matched for age, sex, and premorbid level of physical activity. MAIN MEASURE: Activity diary documenting the exact level of activity during 3 consecutive days during the 1st, 4th, and 12th week post-MTBI (corresponding time intervals for controls) and the Physical Activity Questionnaire. RESULTS: Fifteen pairs of children (MTBI + control) completed the diaries for all 3 assessment times (77% participation rate among consecutive admissions and 44% compliance rate with diary completion). Although some children engaged in activities before the end of the 4-week restriction period, the majority of the children followed the recommendations they received. Children with MTBI maintained a lower level of energy expenditure at the 1- and 4-week evaluations than did the noninjured children, but not at the 12-week evaluation. CONCLUSION: Children with MTBI usually comply with the activity restrictions they receive upon discharge from the hospital.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.541
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.385
Teacher spread0.292 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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