Bibliographic record
Abstract
Concussions across all the age groups are commonly seen by first line health care providers. Initial evaluation should involve a detailed history of injury and past medical history, including previous psychiatric history and head and neck trauma. Physicians may use various tools, including the Sports Concussion Assessment Tool (SCAT)5, Child-SCAT5, and Acute Concussion Evaluation (ACE), to help assess the concussion-related signs and symptoms, focal neurologic findings, and red flags that may require further investigations or help to predict the prolonged recovery. Certain imaging rules, including the Canadian CT Head Rule and the Pediatric Emergency Care Applied Research Network rule, can help guide the need for CT imaging in patients with head injury. However, concussion is ultimately a clinical diagnosis and there is insufficient evidence for utilizing further neuroimaging or biomarkers in routine evaluation, diagnosis, and prognostication. Following diagnosis, after a period of observation, most patients can be discharged with appropriately written and verbal recommendations, as well as outpatient monitoring and follow-up by a physician. Certain prediction algorithms based on patient demographic and clinical factors have been found to better identify high-risk populations for persistent-post-concussive symptoms, although further validation is required.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.027 |
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".