Prognostic factors for persistent symptoms in adults with mild traumatic brain injury: protocol for an overview of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) is an increasing public health problem that can lead to persistent symptoms that have several functional consequences. Understanding the prognosis of a condition is an important component of clinical decision-making and can help guide the prevention of long-term disabilities of patients with mTBI. Several studies and systematic reviews have been conducted in order to understand prognosis of chronic symptoms following mTBI. We aim to synthesize evidence from systematic reviews on factors that affect the risk of persistent symptoms in mTBI-affected adults. METHODS: We will conduct an overview of systematic reviews following steps described in the Cochrane Handbook. We will search in Cochrane, Medline, CINAHL, Embase, PsycINFO, and Epistemonikos for systematic reviews about the prognosis of persistent symptoms following mTBI in the adult population. Two reviewers will independently screen all references and then select eligible reviews based on eligibility criteria. A data extraction grid will be used to extract relevant information. The risk of bias in the included reviews will be assessed using the ROBIS tool. Data will be synthesized into a comprehensive conceptual model in order to have a better understanding of the predictive factors of post-concussion symptoms following mTBI. DISCUSSION: Results will help multiple stakeholders, such as clinicians and rehabilitation program managers, to understand the prognosis of long-term consequences following mTBI. It could guide stakeholders to recognize their patients' prognostic factors and to invest their time and resources in patients who need it the most. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020176676 .
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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 | 0.070 | 0.103 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".