Diagnostic Accuracy of Non-Invasive Thermal Evaluation of Ventriculoperitoneal Shunt Flow in Shunt Malfunction: A Prospective, Multi-Site, Operator-Blinded Study
Bibliographic record
Abstract
BACKGROUND: Thermal flow evaluation (TFE) is a non-invasive method to assess ventriculoperitoneal shunt function. Flow detected by TFE is a negative predictor of the need for revision surgery. Further optimization of testing protocols, evaluation in multiple centers, and integration with clinical and imaging impressions prompted the current study. OBJECTIVE: To compare the diagnostic accuracy of 2 TFE protocols, with micropumper (TFE+MP) or without (TFE-only), to neuro-imaging in patients emergently presenting with symptoms concerning for shunt malfunction. METHODS: We performed a prospective multicenter operator-blinded trial of a consecutive series of patients who underwent evaluation for shunt malfunction. TFE was performed, and preimaging clinician impressions and imaging results were recorded. The primary outcome was shunt obstruction requiring neurosurgical revision within 7 d. Non-inferiority of the sensitivity of TFE vs neuro-imaging for detecting shunt obstruction was tested using a prospectively determined a priori margin of -2.5%. RESULTS: We enrolled 406 patients at 10 centers. Of these, 68/348 (20%) evaluated with TFE+MP and 30/215 (14%) with TFE-only had shunt obstruction. The sensitivity for detecting obstruction was 100% (95% CI: 88%-100%) for TFE-only, 90% (95% CI: 80%-96%) for TFE+MP, 76% (95% CI: 65%-86%) for imaging in TFE+MP cohort, and 77% (95% CI: 58%-90%) for imaging in the TFE-only cohort. Difference in sensitivities between TFE methods and imaging did not exceed the non-inferiority margin. CONCLUSION: TFE is non-inferior to imaging in ruling out shunt malfunction and may help avoid imaging and other steps. For this purpose, TFE only is favored over TFE+MP.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".