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Record W2594471890 · doi:10.2217/cnc-2016-0027

Googling concussion care in the USA: a critical appraisal of online concussion healthcare providers

2017· article· en· W2594471890 on OpenAlexaff
Michael J. Ellis, Lesley Ritchie, Erin Selci, Stephanie Grossi, Samantha Frost, Patrick J. McDonald, Kelly Russell

Bibliographic record

VenueConcussion · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaManitoba Harm Reduction NetworkPan Am ClinicUniversity of ManitobaNeuroDevNetChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsConcussionHealth careMedicineTraumatic brain injuryMedical emergencyHealth professionalsCritical appraisalOccupational safety and healthPoison controlInjury preventionPsychiatryAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

AIM: To examine the online personnel and practice profiles of concussion healthcare providers in the USA. METHODS: We conducted independent, blinded, Google Internet searches for concussion healthcare providers using the terms 'concussion clinic' and 'concussion program' and each American state and completed a critical appraisal of healthcare personnel and services at these websites. RESULTS: A total of 184 concussion healthcare providers were identified. Despite offering care to traumatic brain injury (TBI) patients, access to professionals with expertise in TBI including neuropsychologists (40.8%), neurologists (33.7%) and neurosurgeons (21.7%) was variable across sites. CONCLUSION: Concussion healthcare in the USA is presently delivered by a range of healthcare professionals with varying levels of training in TBI offering a variety of services.

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 imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.116
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.004
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.108
GPT teacher head0.459
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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