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Record W3164799226 · doi:10.1093/neuros/nyab191

In Reply: A Scoping Review of Registered Clinical Studies on Mild Traumatic Brain Injury and Concussion (2000-2019)

2021· review· en· W3164799226 on OpenAlexaff
Julio C. Furlan, Charles H. Tator

Bibliographic record

VenueNeurosurgery · 2021
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Western HospitalToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsConcussionTraumatic brain injuryMedicinePsychologyClinical trialPsychiatryInjury preventionPoison controlMedical emergency

Abstract

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To the Editor: We thank the authors1 for commenting on our manuscript entitled “A scoping review of registered clinical studies on concussion and mild traumatic brain injury (2000-2019).”2 We are pleased to be given the opportunity to respond to their comments, criticisms, and their mini-review of additional small databases not included in our paper, which by design was confined to clinicaltrials.gov, the world's largest clinical trial database to which trial authors in any country can contribute. We will herein deal with their criticisms of a “gap” in our review and that their mini-review represents an improvement. The commentators1 begin by agreeing with us about the importance of a clear definition of mild traumatic brain injury (mTBI) and concussion in future research studies and clinical practice. Our review stressed the importance of clear definitions to aid those who care for patients with mTBI/concussion and seek updated evidence-based medical information to maximize the patients’ neurological and functional recovery, and quality of life. In the field of brain trauma, the terms “mild traumatic brain injury” and “concussion” are often used interchangeably in the medical literature and clinical practice even though the perceptions and stigma associated with each term may have distinct implications for consumers, as we indicated in our review paper.2-4 We acknowledge that there is still a debate in the literature between those who claim concussion is the constellation of symptoms that can arise from any TBI and those who argue that concussion is “a distinct pathophysiological entity with its own diagnostic and management implications.”5 As we stated, concussion describes a more homogenous group of patients than mTBI does, and we endorse that researchers should agree to use the definition of concussion that excludes all lesions identified by routine structural imaging, including computed tomography (CT) or magnetic resonance imaging (MRI). The commentators1 included their own review of 6 other databases in other countries comprising 57 additional studies in mTBI/concussion to add to the 320 studies we reported. This added some valuable information within the focus of our scoping review that reinforces our conclusions, and perhaps expands the global perspective. It was interesting to note that there was a higher overall presence of a definition of mTBI/concussion than we found (61.4% vs 34.1%, respectively). Their mini-review reinforces the need for the use of a common definition, and for reasons already stated, concussion is the preferred term. Unfortunately, they did not provide searchable references to the databases in their mini-review so it was not possible to check them. We were also unable to verify how many different definitions were used in the studies included in their mini-review. Of note, we reported that a definition of mTBI/concussion was eventually obtained from 109 of the 320 studies in our scoping review, which is different from what the commentators labeled “109 different definitions.” Lastly, the commentators outlined possible reasons for the paucity of papers dealing with the prevention of concussions. In our view, the reasons given do not constitute insurmountable obstacles, and researchers should be able to easily overcome these obstacles. There are numerous available study designs that can address key research questions related to the prevention of injuries such as mTBI/concussion that are ethical, lawful, and practical. Notably, Bonnie et al6 led the Committee on Injury Prevention and Control (Division of Health Promotion and Disease Prevention, Institute of Medicine, USA) and wrote the landmark book entitled “Reducing the Burden of Injury: Advancing Prevention and Treatment,” where they included a chapter on the methodological approaches and accomplishments of prevention research. In the context of injury prevention targeting the reduction of frequency or severity of injuries, Bonnie et al6 adapted the seminal work by Haddon et al7 and grouped the prevention interventions into (1) “interventions for changing individual behavior”; (2) “interventions for modifying products or agents of injury”; (3) “interventions for modifying the physical environment”; and (4) “interventions for modifying the sociocultural and economic environment.” As in other fields of research, prevention interventions can be feasibly and satisfactorily evaluated with respect to their efficacy, effectiveness, and cost-effectiveness if the necessary resources are allocated. We are grateful for the commentators’ positive remarks1 about our paper.2 Although the number of trials in mTBI/concussion has steadily risen over the past few years as we have shown, there is much additional work to be done in the prevention, treatment, and prognosis of mTBI/concussion. Funding This study did not receive any funding or financial support. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. Dr Furlan receives salary support from the Wings for Life Spinal Cord Research Foundation.

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)0.050
metaresearch head score (Gemma)0.280
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.950
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.280
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.006
Science and technology studies0.0040.005
Scholarly communication0.0060.011
Open science0.0060.005
Research integrity0.0360.034
Insufficient payload (model declined to judge)0.0070.004

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 head0.506
GPT teacher head0.553
Teacher spread0.047 · 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.

Study designNot applicable
DomainMethods
GenreCommentary

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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