Googling Concussion Care
Bibliographic record
Abstract
BACKGROUND: Concussion is an emerging public health concern, but care of patients with a concussion is presently unregulated in Canada. METHODS: Independent, blinded Google Internet searches were conducted for the terms "concussion" and "concussion clinic" and each of the Canadian provinces and territories. The first 10 to 15 concussion healthcare providers per province were identified. A critical appraisal of healthcare personnel and services offered on the provider's Web site was conducted. RESULTS: Fifty-eight concussion healthcare providers were identified using this search methodology. Only 40% listed the presence of an on-site medical doctor (M.D.) as a member of the clinical team. Forty-seven percent of concussion healthcare providers advertised access to a concussion clinic, program, or center on their Web site. Professionals designated as team leaders, directors, or presidents among concussion clinics, programs, and centers included a neuropsychologist (15%), sports medicine physician (7%), neurologist (4%), and neurosurgeon (4%). Services offered by providers included baseline testing (67%), physiotherapy (50%), and hyperbaric oxygen therapy (2%). CONCLUSIONS: This study indicates that there are numerous concussion healthcare providers in Canada offering diverse services with clinics operated by professionals with varying levels of training in traumatic brain injury. In some cases, the practices of these concussion clinics do not conform to current expert consensus guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".