Accuracy assessment protocols for electromagnetic tracking systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electromagnetic tracking systems have found increasing use in medical applications during the last few years. As with most non-trivial spatial measurement systems, the complex determination of positions and orientations from their underlying raw sensor measurements results in complicated, non-uniform error distributions over the specified measurement volume. This makes it difficult to unambiguously determine accuracy and performance assessments that allow users to judge the suitability of these systems for their particular needs. Various assessment protocols generally emphasize different measurement aspects that typically arise in clinical use. This can easily lead to inconclusive or even contradictory conclusions. We examine some of the major issues involved and discuss three useful calibration protocols. The measurement accuracy of a system can be described in terms of its 'trueness' and its 'precision'. Often, the two are strongly coupled and cannot be easily determined independently. We present a method that allows the two to be disentangled, so that the resultant trueness properly represents the systematic, non-reducible part of the measurement error, and the resultant precision (or repeatability) represents only the statistical, reducible part. Although the discussion is given largely within the context of electromagnetic tracking systems, many of the results are applicable to measurement systems in general.
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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 it