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Record W2772187320 · doi:10.14740/jocmr3173w

Does Red Cell Distribution Width Predict Outcome in Traumatic Brain Injury: Comparison to Corticosteroid Randomization After Significant Head Injury

2018· article· en· W2772187320 on OpenAlexvenueno aff
Farid Sadaka, Nicholas Doctors, Tallia Pearson, Brian Snyders, Jacklyn O’Brien

Bibliographic record

VenueJournal of Clinical Medicine Research · 2018
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlasgow Coma ScaleTraumatic brain injuryHead injuryGlasgow Outcome ScaleRed blood cell distribution widthInternal medicineReceiver operating characteristicRandomizationInjury Severity ScorePoison controlSurgeryInjury preventionRandomized controlled trialEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability. The role of red cell distribution width (RDW) as a prognostic biomarker for outcome in TBI patients is unknown. Based on the corticosteroid randomization after significant head injury (CRASH) trial database, a prognosis calculator (CRASH) has been developed for outcome prediction in TBI. The objectives of this study are to investigate the association between RDW on day 1 of TBI and outcome, and to compare outcome prediction from RDW to that from CRASH. METHODS: We performed a retrospective review of patients with TBI and a Glasgow coma scale (GCS) score of 14 or less. Day 1 RDW and CRASH data were extracted. CRASH was calculated for each patient. Outcome was defined as mortality at 14 days and GOS at 6 months, with poor outcome defined as GOS of 1 - 3. Patients were stratified according to RDW values into six groups, and according to CRASH values into six groups. RESULTS: A total of 416 patients with TBI were included, with 339 survivors (S) and 77 non-survivors (NS). Compared to survivors, non-survivors were of similar age in years (58 ± 23 vs. 58 ± 23, P = 1.0), had lower GCS scores (5 ± 3 vs. 12 ± 3, P = 0.0001), similar RDW (14.0 ± 1.2 vs. 13.9 ± 1.5, P = 0.6), and higher CRASH values (68 ± 26 vs. 24 ± 22, P = 0.0001). Estimating the receiver-operating characteristic (ROC) area under the curve (AUC) showed that CRASH was a significantly better predictor of mortality compared to RDW (AUC = 0.91 ± 0.01 for CRASH compared to 0.66 ± 0.03 for RDW; P < 0.0001). In addition, CRASH was a better predictor of neurologic outcome compared to RDW (AUC = 0.85 ± 0.02 for CRASH compared to 0.76 ± 0.03 for RDW; P = 0.005). CONCLUSIONS: CRASH calculator was a strong predictor of mortality in patients with TBI. RDW on day 1 did not differ between survivors and non-survivors, and was a poor predictor of mortality. Both RDW on day 1 and CRASH calculator are good predictors of 6-month outcome in TBI patients, although CRASH calculator remains a better predictor.

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)0.007
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.159
GPT teacher head0.523
Teacher spread0.364 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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