What factors must be considered in ‘return to school’ following concussion and what strategies or accommodations should be followed? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the evidence regarding (1) factors affecting return to school (RTS) and (2) strategies/accommodations for RTS following a sport-related concussion (SRC) in children and adolescents. DESIGN: A systematic review of original studies specifically addressing RTS following concussion in the paediatric and sporting context. DATA SOURCES: MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid) electronic databases and the grey literature OpenGrey, ClinicalTrials.gov and Google Advanced. ELIGIBILITY CRITERIA: Studies were included if they were original research on RTS following SRC in children aged 5-18 years published in English between 1985 and 2017. RESULTS: A total of 180 articles were identified; 17 articles met inclusion criteria. Several factors should be considered for RTS after concussion, including: symptomatology; rest following injury; age/grade; and course load. On RTS after concussion, 17%-73% of students were provided academic accommodations or experienced difficulty with RTS. Students were more likely to obtain academic accommodations in schools with a concussion policy if they had a medical RTS letter and had regular medical follow-up after concussion. CONCLUSIONS: Schools should have a concussion policy and offer individualised academic accommodations to students recovering from SRC on RTS; a medical letter should be provided to facilitate provision/receipt of academic accommodations; students should have early, regular medical follow-up following SRC to help with RTS and monitor recovery; students may require temporary absence from school after SRC; clinicians should assess risk factors/modifiers that may prolong recovery and require more intensive academic accommodations. PROSPERO REGISTRATION NUMBER: CRD42016039184.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".