Persistent Postconcussion Symptoms: An Expert Consensus-Based Definition Using the Delphi Method
Bibliographic record
Abstract
OBJECTIVE: To provide an expert consensus definition of persistent postconcussion symptoms following a mild traumatic brain injury (mTBI). PARTICIPANTS: Canadian healthcare professionals caring for patients with mTBI. DESIGN: Online Delphi process. MAIN MEASURES: A first Delphi round documented important dimensions or criteria to consider when defining persistent symptoms. Expert opinions were then resubmitted in 4 subsequent Delphi rounds and their relevance was rated using a 9-point Likert scale. An item with a median rating of 7 or more and a sufficient level of agreement were considered consensual. RESULTS: After 5 rounds, consensus was reached on a set of criteria that can be summarized as follows: presence of any symptom that cannot be attributed to a preexisting condition and that appeared within hours of an mTBI, that is still present every day 3 months after the trauma, and that has an impact on at least one sphere of a person's life. CONCLUSION: This Delphi consensus proposes a set of criteria that support a more uniform definition of persistent symptoms in mild TBI among clinicians and researchers. This definition may help clinicians better identify persistent postconcussion symptoms and improve patient management.
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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 | 0.184 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".