MétaCan
Menu
Back to cohort
Record W4256098837 · doi:10.5858/2003-127-783-ir

In Reply

2003· article· en· W4256098837 on OpenAlexaff
Theodore E. Warkentin

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHeparin-induced thrombocytopeniaLikelihood ratios in diagnostic testingPlateletOrthopedic surgeryMedicineAntibodyPlatelet activationDiagnostic testCardiac surgeryArea under the curveInternal medicineAntigenImmunologyReceiver operating characteristicCardiologySurgeryEmergency medicine

Abstract

fetched live from OpenAlex

In Reply.—Dr Breddin states “the presently available tests [for heparin-induced thrombocytopenia] have no or very little diagnostic value.” I disagree. Heparin-induced thrombocytopenia (HIT) antibody tests are among the most diagnostically useful assays in immunohematology.12Diagnostic usefulness of an assay can be represented by plotting sensitivity-specificity tradeoffs at various cutoffs between negative and positive assay results (operating characteristics). We have studied performance characteristics of the 2 major classes of assays, a platelet activation assay (platelet serotonin-release assay) and a PF4-dependent antigen assay, in 2 patient populations at high risk of HIT (postoperative orthopedic and cardiac surgery patients).1 The Figure shows the operating characteristics we found.For comparison, curve A is shown (Figure), which represents a “useless” assay without diagnostic information. Indeed, such poor operating characteristics have been reported for certain assays of platelet-associated immunoglobulin G to diagnose immune thrombocytopenia.3 Dr Breddin's comments imply that this ought to be the profile of HIT antibody tests. However, curves B and C show the operating characteristics of the activation and antigen assays, respectively, for diagnosis of HIT in post–orthopedic surgery patients. Curves D and E show the corresponding curves in post–cardiac surgery patients.The extent to which a given test result alters the physician's estimate of the pretest probability of HIT is known as the likelihood ratio, which is defined as Sensitivity/(1 − Specificity).4 Thus, for a post–cardiac surgery patient with a strong positive platelet-activation result (eg, 90% serotonin release), the likelihood ratio is about 20 (see the open circle on curve D, indicating 0.70/[1 − 0.965] = 20). Thus, if the physician had estimated a pretest probability of 50% (odds of 0.5:0.5), then this test result increases the posttest probability to more than 95% (0.5:0.5 × 20:1 = 20:1, or 95.2%). In contrast, the high sensitivity of this assay to detect clinically significant HIT antibodies (>95%) means that a negative test result lowers the posttest probability to less than 5%.The diagnostic impact of such a strong positive result is even greater in post–orthopedic surgery patients, for whom the corresponding likelihood ratio is 90. As before, a negative test result essentially rules out HIT.Although the antigen assay detects more clinically insignificant antibodies than the activation assay, it remains diagnostically useful. Likelihood ratios for a strong positive test (eg, optical density of 1.5) range from 10 to 40 for post–cardiac and post–orthopedic surgery patients, respectively. Also, its high sensitivity (>95%) means that a negative test generally rules out HIT.Finally, Dr Breddin's inferences regarding test specificity (2%–30%) are incorrect. As clinically insignificant HIT antibodies are detected by antigen assay in 10% to 50% of post–orthopedic and post–cardiac surgery patients, respectively, the corresponding specificities range from 50% to 90%.1 (Specificity is defined as the proportion of individuals without HIT who have a negative test.) The corresponding specificity values for the activation assay range from 85% to 95%.1 If one considers that most patients with clinical HIT have quantitative test results far higher than the conventional cutoff between negative and positive,2 the practical specificity of these assays—as expressed by likelihood ratios—is even greater.

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)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.043
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0220.029
Insufficient payload (model declined to judge)0.0430.036

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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueArchives of Pathology & Laboratory MedicineSame topicHeparin-Induced Thrombocytopenia and ThrombosisFrench-language works237,207