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.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".