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Inter-Observer Variability in the Assessment of the 4Ts Score for the Diagnosis of Heparin Induced Thrombocytopenia (HIT) in Patients Undergoing Cardiac Surgery (CS).

2009· article· en· W2590572145 on OpenAlexaff
Fareen Din, Michael J. Kovacs, Ron Butler, Alejandro Lazo‐Langner

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsHeparin-induced thrombocytopeniaMedicineThrombosisPopulationComplicationCardiac surgeryRetrospective cohort studySurgeryInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Abstract 4467 Background HIT is a potentially serious complication of heparin therapy. Because of the multiple conditions potentially causing thrombocytopenia, particularly in clinical scenarios such as patients undergoing CS or admitted to intensive care units, its diagnosis is frequently not straightforward and depends on a combination of clinical suspicion and laboratory tests. The 4Ts score has been proposed as a tool to assess the pretest probability of a HIT diagnosis (as low, intermediate or high) and it comprises 4 clinical parameters: severity and timing of onset of thrombocytopenia, development of thrombosis, and the presence of alternate causes for thrombocytopenia. This score has several handicaps including the fact that the temporal profile of thrombocytopenia onset is frequently unclear, the history of previous heparin exposure is not always available, the diagnosis of new thrombosis (including extensions of previous ones) is often difficult, and the judgment about the likelihood of an alternate cause for the thrombocytopenia is entirely subjective. The clinical usefulness of a scoring system depends mainly on its robustness and reproducibility and therefore inclusion of difficult to evaluate or subjective components might compromise its clinical performance. Objectives We evaluated the inter-observer agreement for classifying the probability of HIT in a population of patients undergoing CS. Methods We conducted a retrospective study of patients admitted for cardiac surgery to our institution between January 2006 and December 2008 and in whom HIT was suspected. Clinical information including all necessary data for calculating the 4 components of the 4Ts score was collected in a standardized database which was used by 2 independent observers (blinded to serological tests results) to calculate the score and the pretest probability for HIT as low (≤3 points), intermediate (4-5 points), or high (≥6 points). Scores assigned by both observers were compared using a Wilcoxon signed ranks test and the inter-observer agreement for scores and pretest probabilities was evaluated using a Kappa statistic. 95% confidence intervals for proportions were estimated using the Wilson score method. Results 73 patients were included in the analysis. The score assigned by both observers differed in 40 cases (54.8%; 95% CI 43.4, 65.7; p=0.001). The value of the Kappa statistic for the inter-observer agreement for scores was 0.266 (95% CI 0.111, 0.421) and for pretest probabilities was 0.400 (95% CI 0.218, 0.582). Conclusions In this study, we found that the 4Ts score has an inter-observer agreement ranging between slight and moderate when applied to patients undergoing CS. The variability observed in the assessment of the score raises doubts about its usefulness when evaluating the possibility of HIT in this patient population. Limitations of this study include a relatively small sample size, inclusion of a single clinical setting, and its retrospective nature. Further studies in this and other populations are needed to assess the reproducibility of the 4Ts score. Disclosures: No relevant conflicts of interest to declare.

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)0.020
metaresearch head score (Gemma)0.050
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 score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
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 head0.059
GPT teacher head0.314
Teacher spread0.256 · 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
Published2009
Admission routes1
Has abstractyes

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