Liquid light guides versus fiber light guides in clinical near-infrared spectroscopy
Bibliographic record
Abstract
New commercial liquid light guides have an advantage over fiberoptic bundles regarding breakage during clinical handling. We investigate the quality of clinical data collection using liquid versus fiber bundles as receivers. A four-wavelength NIRO-500 near-IR spectrophotometer is used with single-terminal fiber bundles, multiterminal fiber bundles, or a single-terminal liquid light guide as receivers. Repeated 3-min trials are done using a stable phantom, an unstable phantom, and the human forearm. A least-squares linear best-fit line and its root mean square error (RMSE), a measure of signal noise, are derived for each wavelength of each trial. The mean and standard deviations for the RMSEs of the single-terminal fiber optic receiving cable are derived for comparison standards. The liquid light guides have 51 to 174% greater signal noise with RMSEs 2 to 12 standard deviations above the mean of the single-terminal fiber bundle. The multiterminal fiber bundles have 49% less to 32% greater signal noise and had RMSEs within 1 to 4 standard deviations above the mean of the single-terminal fiber bundle. These comparisons suggest fiber optic bundles are preferable for clinical near-IR spectroscopy (NIRS) applications requiring low signal noise.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".