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Record W2332825642 · doi:10.1097/htr.0000000000000223

Impact of Early Follow-Up Intervention on Parent-Reported Postconcussion Pediatric Symptoms: A Feasibility Study

2016· article· en· W2332825642 on OpenAlexaff
Patricia Mortenson, Alexander R. Hengel, Jacqueline Purtzki

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsIntervention (counseling)MedicinePediatricsPsychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the effectiveness and feasibility of early intervention telephone counseling with parents in limiting postconcussion symptoms and impacts on children and youth. SETTING: Recruitment occurred postdischarge from one pediatric emergency department. PARTICIPANTS: Sixty-six parents of children aged 5 to 16 years with a diagnosis of a concussion injury. DESIGN: A pilot, randomized controlled study compared the efficacy of telephone counseling (reviewing symptom management and return to activity with parents at 1 week and 1 month postinjury) with usual care (no formalized follow-up). MAIN MEASURES: The Post-Concussion Symptom Inventory and the Family Burden of Injury Interview administered with parents by a blinded therapist at 3 months postinjury. RESULTS: No significant difference between the groups at 3 months postinjury in postconcussion symptoms (P = .67) and family stress (P = .647). CONCLUSION: The findings suggest that the early counseling intervention strategy trialed herein may not be effective for children and youth who experience significant postconcussion symptoms. Further research is needed to determine whether more intensive and integrated care would better serve children.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.083
GPT teacher head0.431
Teacher spread0.348 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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