MétaCan
Menu
← Back to cohort
Record W2620829678 · doi:10.1017/cjn.2017.101

P.016 Early telephone follow-up for traumatic brain injury patients using the Rivermead post-concussion symptoms questionnaire

2017· article· en· W2620829678 on OpenAlexvenueno aff
Ginette Thibault-Halman, Lynne Fenerty, Patricia Taylor, Nelofar Kureshi, Simon Walling, DB Clarke

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsRivermead post-concussion symptoms questionnaireMedicineNeurosurgeryTraumatic brain injuryConcussionPhysical therapyRehabilitationInjury preventionPoison controlPsychiatryEmergency medicine

Abstract

fetched live from OpenAlex

Background: Patients who require hospitalization for a mild or moderate traumatic brain injury (TBI) are often discharged home with uncertainty around their full recovery. This study examines the frequency and severity of common post-TBI symptoms, as assessed by the Rivermead Post-Concussion Symptoms Questionnaire (RPCQ). Methods: All adult TBI inpatients discharged home from the Neurosurgery service were interviewed by phone at two weeks by a rehab-based nurse practitioner. RPCQ components (cognitive, emotional, and somatic) were analyzed; findings and management recommendations were communicated to family practitioners and the treating neurosurgeon. Results: In 46 patients, cognitive symptoms were present in 52%, 91% had somatic, and 100% had emotional symptoms. Fatigue was the most common symptom (67%). Double vision was the least common symptom (4%). Recommendations for managing symptoms, return to work, and need for formal clinical assessment were provided for 37% of cases. Conclusions: All patients admitted to neurosurgery with mild or moderate TBI had symptoms at two weeks. The RPCQ is a low-cost structured evaluative tool which highlights needs and provides guidance for patients and care-givers; it also seems effective in identifying those who may require formal clinical assessment.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.072
GPT teacher head0.343
Teacher spread0.270 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicTraumatic Brain Injury Research→French-language works237,207→