Bibliographic record
Abstract
OBJECTIVE: A 12-year-old girl presented to our pediatric emergency department after a head injury with symptoms of concussion and acute stuttering. A PubMed search identified only 1 similar pediatric case. We investigated whether new-onset stuttering may be seen in the presence of acute concussive symptoms using an infodemiologic approach. METHODS: We conducted a search with a metabrowser search engine (www.dogpile.com) using the free-text words "concussion" and "stuttering." The first 100 hits were scanned specifically for forum posts, extracting reports of concussions that were followed by new-onset stuttering. Duplicates were minimized by cross-referencing user name, location, date, and reported age and sex. RESULTS: Of the first 100 hits, we identified 17 unique posts that described an injury leading to a concussion followed within a short interval by new-onset stuttering. Posts were primarily by the affected individual (76%) and 64% involved female individuals. Sports and falls/injury accounted for most injuries (71%). Forty-one percent of posts explicitly stated that the concussion had been formally diagnosed. For those that reported the timing of stuttering onset (47%), the stuttering was documented within 1 hour of the injury (4/8) or between 1 and 24 hours (4/8). CONCLUSIONS: The ease with which we found so many reports of stuttering after head injury with concussive symptoms confirms that new-onset stuttering may be a symptom of concussion. Our experience highlights both a failure of using conventional medical literature and a success of using nontraditional information sources in identifying uncommonly associated symptoms of frequently encountered conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".