A Predictive Model for Temporal Artery Biopsy in the Setting of Suspected Giant Cell Arteritis: A Validation Study
Bibliographic record
Abstract
PURPOSE: A previously published predictive model based on threshold parameters for erythrocyte sedimentation rate, c-reactive protein, and platelet count demonstrated that 40% of patients who underwent biopsy may not have required it. The current study was performed to evaluate the model's performance on an independent data set. METHODS: This is a retrospective consecutive series of patients undergoing temporal artery biopsy (TAB) in a single health region in Canada. The model was applied to a multicenter cohort of patients undergoing TAB by a variety of surgical services. A centralized pathological database serving multiple institutions and surgical services was used to identify patients undergoing TAB. RESULTS: Over a 7-year period, patients undergoing TAB were identified via a central pathological database. Those who had concurrent illnesses which would likely affect erythrocyte sedimentation rate, c-reactive protein, and platelet count, patients on steroids for >2 weeks by the time of biopsy, and those with missing serum markers were excluded. The previously developed model was applied to the 222 patients enrolled. The model correctly identified 29% of patients with a pretest probability of 0% for a positive biopsy and 9% with a pretest probability of 100%, suggesting that in total, 38% of patients could have avoided TAB. CONCLUSION: The results of this independent data set support the previously published predictive formula. Utilizing a simple, clinically applicable predictive model of the pretest probabilities, approximately 38% of TAB currently being performed may be avoided. The results suggest that evaluation with a prospective multicentre study would be appropriate.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".