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Record W4214840276 · doi:10.1503/cjs.007819

Intensity of hospital resource use following traumatic brain injury: a multicentre cohort study, 2013–2016

2022· article· en· W4214840276 on OpenAlexafffundvenueabout
Coralie Assy, Lynne Moore, Teegwendé Valérie Porgo, Imen Farhat, Pier‐Alexandre Tardif, Catherine Truchon, Henry T. Stelfox, Belinda J. Gabbe, François Lauzier, Alexis F. Turgeon, Julien Clément

Bibliographic record

VenueCanadian Journal of Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversité LavalInstitut National d'Excellence en Santé et en Services SociauxThe Quebec Population Health Research Network
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMedicineIncidence (geometry)Confidence intervalResource useRetrospective cohort studyEmergency medicineCohortIntraclass correlationCohort studyInternal medicine

Abstract

fetched live from OpenAlex

Background: The knowledge gap regarding acute care resource use for patients with traumatic brain injury (TBI) impedes efforts to improve the efficiency and quality of the care of these patients. Our objective was to evaluate interhospital variation in resource use for patients with TBI, identify determinants of high resource use and assess the association between hospital resource use and clinical outcomes. Methods: We conducted a multicentre retrospective cohort study including patients aged 16 years and older admitted to the inclusive trauma system of Quebec following TBI, between 2013 and 2016. We estimated resource use using activity-based costs. Clinical outcomes included mortality, complications and unplanned hospital readmission. Interhospital variation was evaluated using intraclass correlation coefficients (ICCs) with 95% confidence intervals (CIs). Correlations between hospital resource use and clinical outcomes were evaluated using correlation coefficients on weighted, risk-adjusted estimates with 95% CIs. Results: We included 6319 patients. We observed significant interhospital variation in resource use for patients discharged alive, which was not explained by patient case mix (ICC 0.052, 95% CI 0.043 to 0.061). Adjusted mean resource use for patients discharged to long-term care was more than twice that of patients discharged home. Hospitals with higher resource use tended to have a lower incidence of mortality (r −0.347, 95% CI −0.559 to −0.087) and unplanned readmission (r −0.249, 95% CI −0.481 to 0.020) but a higher incidence of complications (r 0.491, 95% CI 0.255 to 0.666). Conclusion: Resource use for TBI varies significantly among hospitals and may be associated with differences in mortality and morbidity. Negative associations with mortality and positive associations with complications should be interpreted with caution but suggest there may be a trade-off between adverse events and survival that should be evaluated further.

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)0.002
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 score0.649
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.042
GPT teacher head0.266
Teacher spread0.224 · 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".

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Citations3
Published2022
Admission routes4
Has abstractyes

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