Low failure rate reported of diagnosis algorithm for suspected upper extremity deep vein thrombosis
Bibliographic record
Abstract
Commentary on: Kleinjan A, Di Nisio M, Beyer-Westendorf J, et al. Safety and feasibility of a diagnostic algorithm combining clinical probability, d-dimer testing and ultrasonography for suspected upper extremity deep venous thrombosis: a prospective management study. Ann Intern Med 2014;160:451–7.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Upper extremity deep vein thrombosis (UEDVT) is an infrequent type of venous thromboembolism with an estimated incidence of 0.4–1 case per 10 000 persons.1 UEDVT may cause pulmonary embolism but this risk is lower than with lower extremity DVT. Accurate ruling out of UEDVT is mandatory in order to avert unnecessary exposure to anticoagulation therapy. Unlike DVT of the lower limbs or pulmonary embolism, there is no validated diagnostic algorithm combining clinical probability assessment, D-dimer testing and compression ultrasonography to rule out UEDVT.2 ,3 This was a multicentre study evaluating a diagnostic algorithm … [1]: {openurl}?query=rft.jtitle%253DAnn%2BIntern%2BMed%26rft.issn%253D0003-4819%26rft.volume%253D160%26rft.spage%253D451%26rft_id%253Dinfo%253Adoi%252F10.7326%252FM13-2056%26rft_id%253Dinfo%253Apmid%252F24687068%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.7326/M13-2056&link_type=DOI [3]: /lookup/external-ref?access_num=24687068&link_type=MED&atom=%2Febmed%2F19%2F5%2F189.atom [4]: /lookup/external-ref?access_num=000334093800002&link_type=ISI
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".