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Record W3157493030 · doi:10.1101/2021.01.19.21250027

Natural Progression of Routine Laboratory Markers following Spinal Trauma: A Longitudinal, Multi-Cohort Study

2021· preprint· en· W3157493030 on OpenAlexaff
Lucie Bourguignon, Anh Khoa Vo, Bobo Tong, Fred H. Geisler, Orpheus Mach, Doris Maier, John L. K. Kramer, Lukas Grassner, Catherine R. Jutzeler

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsVancouver Coastal HealthUniversity of SaskatchewanInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersWings for LifeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicineHematocritPathologicalConfoundingInternal medicineAnalysis of varianceLongitudinal studyClinical trialCohortCohort studyPathology

Abstract

fetched live from OpenAlex

ABSTRACT Objective To track and quantify the natural course of hematological markers over the first year following spinal cord injury. Methods Data on hematological markers, demographics, and injury characteristics were extracted from medical records of a clinical trial (Sygen) and an ongoing observational cohort study (Murnau Study). The primary outcomes were concentration/levels/amount of commonly collected hematological markers at multiple time-points. Two-way ANOVA and mixed-effects regression techniques were used to account for the longitudinal data and adjust for potential confounders. Trajectories of hematological markers contained in both data sources were compared using the slope of progression. Results At baseline (≤ 2 weeks post-injury), most hematological markers were at pathological levels, but returned to normal values over the course of six to twelve months post-injury. The baseline levels and longitudinal trajectories were dependent on injury severity. More complete injuries were associated with more pathological values (e.g. hematocrit, ANOVA test; Chisq = 77.10, df = 3, adjusted p-value<0.001, and Chisq = 94.67, df = 3, adjusted p-value<0.001, in the Sygen and Murnau studies, respectively). Comparing the two databases revealed some differences in the hematological markers, which are likely attributable to differences in study design, sample size, and standard of care. Conclusions Due to trauma-induced physiological perturbations, hematological markers undergo marked changes over the course of recovery, from initial pathological levels that normalize within a year. The findings from this study are important as they provide a benchmark for clinical decision making and prospective clinical trials. All results can be interactively explored on the Haemosurveillance website ( https://jutzelec.shinyapps.io/Haemosurveillance/ ). Code availability https://github.com/jutzca/Systemic-effects-of-Spinal-Cord-Injury

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)0.003
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.058
GPT teacher head0.415
Teacher spread0.357 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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