Validity of Infrared Thermal Measurements of Segmental Paraspinal Skin Surface Temperature
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the validity of thermal measurements by infrared camera thermometry. METHODS: Seventeen subjects underwent a 30-minute acclimatizing period in a controlled environment room. Thermal recordings were executed at the levels of C4 and L4. Fifteen recordings per segment were acquired in an alternating mode that always started at L4. Each subject was required to participate on 5 occasions. The exclusion criteria for the subjects included the following: no inflammatory disease or fever, no consumption of beverages containing caffeine, and no participation in physical activity 2 hours before the recording session; female subjects could not be menstruating on a day of recording. RESULTS: A total of 2550 recordings for the cervical area and the lumbar area was recorded. Strong significant correlations were found for the left (r = .77) and right (r = .71) lumbar sections (P < .0001) whereas weaker significant correlations were observed for the left (r = .56) and right (r = .63) cervical areas (P < .0001). The limits of agreement (Bland-Altman) showed good relationships but poor interchangeability. CONCLUSIONS: In this study, the infrared cameras showed that they were valid tools in a controlled environment; however, the technique for the cervical measurements needs to be reassessed.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".