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Record W2523660422 · doi:10.2217/cnc-2016-0009

Identification of hidden health utilization services and costs in adults awaiting tertiary care following mild traumatic brain injury in Toronto, Ontario, Canada

2016· article· en· W2523660422 on OpenAlexaffabout
Cindy Hunt, Katrina Zanetti, Brian Kirkham, Alicja Michalak, Cheryl Masanic, Chantal Vaidyanath, Shree Bhalerao, Michael D. Cusimano, Andrew Baker, Donna Ouchterlony

Bibliographic record

VenueConcussion · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation InstitutePublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTraumatic brain injuryReferralMedicineHealth careTertiary careEmergency medicinePsychiatryFamily medicine

Abstract

fetched live from OpenAlex

AIM: The cognitive, emotional, behavioral and physical impairments experienced by adults after mild traumatic brain injury (mTBI) can produce substantial disability, with 15-20% requiring referral to tertiary care (TC) for persistent symptoms. METHODS: A convenience sample of 201 adult patients referred to TC as a result of mTBI was studied. Self-reported data were collected at first TC visit, on average 10 months postinjury. Patients reported the type and intensity of healthcare provider visit(s) undertaken while awaiting TC. RESULTS: On average males reported 37 and females 30 healthcare provider visits, resulting in over $500,000 Canadian dollars spent on potentially excess mTBI care over 1 year. DISCUSSION: Based on conservative estimate of 15% of mTBI patients receiving TC, this finding identifies a possible excess in care of $110 million for Ontario. Accurate diagnosis of mTBI and early coordination of follow-up care for those needing TC could increase cost-effectiveness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.338
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.341
Teacher spread0.307 · 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 teacher head, 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

Citations34
Published2016
Admission routes2
Has abstractyes

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