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Record W1847377072 · doi:10.26443/mjm.v11i1.558

Injury Severity Score (ISS) vs. ICD-derived Injury Severity Score (ICISS) in a patient population treated in a designated Hong Kong trauma centre

2020· article· en· W1847377072 on OpenAlexaffvenue
Sydney Sek Ning Wong, Gkk Leung

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsQueen's University
FundersQueen Mary University of London
KeywordsInjury Severity ScoreMedicineReceiver operating characteristicAbbreviated Injury ScaleInjury preventionPoison controlPopulationRevised Trauma ScoreOccupational safety and healthMajor traumaEmergency medicineInternal medicineMedical emergencyPathology

Abstract

fetched live from OpenAlex

Trauma and Injury Severity Score (TRISS) has been the benchmark of mortality risk in trauma centers for over 30 years. TRISS utilizes the Injury Severity Score (ISS) as an index of anatomical injury. This study investigated the efficacy of a new type of index of anatomical injury called the ICD-derived Injury Severity Score (ICISS) compared to the ISS using a logistic regression analysis and a global chi-square test of the areas under the Receiver Operator Characteristic (ROC) curves. We found that the empirically derived ICISS performed as well as the consensus derived ISS with no statistical differences between their respective area under the ROC curves.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.287
Teacher spread0.240 · 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.

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

Citations28
Published2020
Admission routes2
Has abstractyes

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