Health-Related Quality of Life after Pediatric Mild Traumatic Brain Injury/Concussion: A Systematic Review
Bibliographic record
Abstract
Health-related quality of life (HRQOL) is an emerging method to quantify the consequences of pediatric mild traumatic brain injury (mTBI)/concussion in both clinical practice and research. However, to utilize HRQOL measurements to their full potential in the context of mTBI/concussion recovery, a better understanding of the typical course of HRQOL after these injuries is needed. The objective of this study was to summarize current knowledge on HRQOL after pediatric mTBI/concussion and identify areas in need of further research. The following databases from their earliest date of coverage through June 1, 2015 were used: MEDLINE(®), PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), SPORTDiscus, Cochrane Central Register of Controlled Trials (CENTRAL), Physiotherapy Evidence Database (PEDro), and Child Development and Adolescent Studies (CDAS). Studies must have examined and reported HRQOL in a pediatric population after mTBI/concussion, using a validated HRQOL measurement tool. Eight of 1660 records identified ultimately met inclusion criteria. Comprehensive data were extracted and checked by a second reviewer for accuracy and completeness. There appears to be a small but important subgroup of patients who experience poor HRQOL outcomes up to a year or longer post-injury. Potential predictors of poor HRQOL include older age, lower socioeconomic status, or a history of headaches or trouble sleeping. Differing definitions of mTBI precluded meta-analysis. HRQOL represents an important outcome measure in mTBI/concussion clinical practice and research. The evidence shows that a small but important proportion of patients have diminished HRQOL up to a year or longer post-injury. Further study on this topic is warranted to determine the typical longitudinal progression of HRQOL after pediatric concussion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".