Necessity of Temporal Artery Biopsy for Giant Cell Arteritis: A Systematic Review
Bibliographic record
Abstract
Background: Temporal artery biopsy (TAB) is currently the gold standard procedure to diagnose giant cell arteritis. Despite low sensitivity, TAB is routinely performed even if a clinical diagnosis has already been made. The objective of this study was to determine the usefulness of TAB for giant cell arteritis management. Methods: We performed a systematic review to identify studies that compared steroid treatment between TAB+ and TAB− patients. EMBASE, MEDLINE, and the Cochrane Central Register of Controlled Trials were searched from inception until April 4, 2020. Titles, abstracts, and full texts were reviewed by two independent reviewers and conflicts resolved by consensus. Studies reporting TAB result and steroid treatment were included. Information pertaining to steroid treatment was compared between TAB+ and TAB− groups. Steroid duration was compared by grouping patients in a less than 6 month group, a 6–24 month group, and a more than 24 month group. Results: An estimated 5288 abstracts were screened and 13 studies involving 1355 patients were included. Rate of prebiopsy steroid treatment was higher in TAB+ patients compared with TAB− patients [93% versus 63% (P < 0.001)]. The TAB+ group was more likely to be started on steroids prebiopsy [28% versus 8% (P < 0.001)]. TAB+ and TAB− patients had similar steroid duration for all groups [<6-month group 17% versus 19% (P-0.596), the 6-24-month group 16% versus 19% (P-0.596), and the >24-month group 66% versus 63% (P-0.642)]. Conclusion: TAB results have minimal impact on treatment, and the utility should be reconsidered when a clinical diagnosis of giant cell arteritis is possible.
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How this classification was reachedexpand
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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".