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Record W7120362736

Validation of clinical model for the diagnosis of lower extremity deep vemous trombosis

2001· article· pt· W7120362736 on OpenAlexaboutno aff
Edvaldo [UNIFESP] De Souza

Bibliographic record

VenueUNIFESP Institutional Repository (Universidade Federal de São Paulo) · 2001
Typearticle
Languagept
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDeep veinPulmonary embolismIncidence (geometry)ThrombosisClinical PracticeAnticoagulant therapyProspective cohort studySigns and symptoms
DOInot available

Abstract

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Introduction: The deep vein thrombosis (DVT) has as serious complications lung embolism, important cause of mortality, and post-thrombosis syndrome ,the most frequent cause of chronic vein stasis of the lower limbs. The signs and clinical symptoms of DVT present a high rate of false-positive and false-negative, when compared to objective methods of diagnosis. The correct diagnosis of DVT, confirmed by phlebography or other non-invasive methods , permits the appropriate treatment with anticoagulants, reducing the incidence of lung embolism and minimizing chronic vein stasis. It also avoids the unnecessary exposure to the risks of anticoagulant therapy in the negative cases. With the indiscriminate use of subsidiary exams, the incidence of negative exams has increased, reducing the cost-benefit of these diagnostic methods. Philip S. Wells, of the University of Ottawa, Canada, in 1995 and 1997, proposed a method of clinical prediction for the diagnosis of DVT, and he concluded that it is possible to stratify groups accurately into high, moderate and low probability, rationalizing the use of supplementary diagnostic methods, method that needs validation in other centers, as suggested by the author himself. Objective: To test the hypothesis that the model of clinical prediction proposed by Wells is capable of stratifying the patients into groups of high, moderate and low probability of DVT of the lower limbs. Method: Prospective study, including 111 consecutive patients, 114 members, with signs and symptoms of DVT in the lower limbs. Of these, 99 carried out phlebography, resulting in 102 extremities studied. The patients were examined according to the order of their arrival in the hospital or by the request of intra-hospital evaluation of patients admitted for other reasons. A postgraduate student of vascular surgery, a second year resident of General Surgery, and a second year medical student, who had never had contact with patients with DVT, filled out forms based on the proposal by Wells, and would not have further contact with the examined patient. The phlebography were carried out by doctors that didn`t know about the forms and were just interpreted at the end of the study, by three other assisting doctors that didn`t know the identity of the patients and had not participated in the treatment or previous evaluation. Results: In 65 (63,7%) of the 102 lower limbs the presence of DVT was proven by phlebography. The clinical model of Wells demonstrated a prevalence of DVT of 85,5% in the category of high probability, 50% in the group of moderate probability and 25% in the category of low probability. The location of DVT was proximal, starting from the popliteal vein, by 80,6%, 25% and 12,5%, while it was located exclusively in the veins of the calf by 4,8%, 25,0% and 12,5%, in the high, moderate and low probability groups, respectively. The coefficient of reproducibility of Cronbach among the postgraduate, the resident and the student was 86,3%. Conclusion: The model of clinical prediction of DVT proposed by Wells allows adequate identification of patients with high probability and with DVT proximal. However, the method is unsatisfactory for the identification of DVT in the patients allocated in the moderate and low probability groups.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.313
Teacher spread0.263 · 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 designSimulation or modeling
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
Published2001
Admission routes1
Has abstractyes

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