Agreement Between an Ingestible Telemetric Sensor System and a Mercury Thermometer Before and After Linear Regression Correction
Bibliographic record
Abstract
OBJECTIVES: To evaluate the use of the CorTemp ingestible sensor system to monitor athletes in the prevention of thermal injuries, 3 objectives were established: (1) to determine the agreement between the system and a mercury thermometer and quantify the effect of exceeding the recommended yearly manufacturer calibration, (2) to establish the effect of individual sensor correction on agreement, and (3) to determine the quantity of data required for effective correction. DESIGN: Validation study. PARTICIPANTS: Ninety-four ingestible sensors. INTERVENTIONS: (1) Five comparisons were made between each sensor and a mercury thermometer across the range of 33 to 41 degrees C. This was performed immediately, and 14 to 18 months, after factory calibration. (2) Linear regression equations were created and used to correct sensor readings at approximately 37 degrees C; the corrected value was compared with the mercury thermometer. (3) Linear regression equations were created for each sensor using 2 to 5 data points. MAIN OUTCOME MEASURES: Systematic bias and random error (95%). RESULTS: (1) Systematic bias + or - random error (95%) was 0.73% + or - 0.23% ( approximately 0.27 + or - 0.09 degrees C) and 0.54% + or - 0.28% ( approximately 0.22 + or - 0.11 degrees C) immediately and 14 to 18 months post factory calibration, respectively. (2) Regression correction improved agreement through reductions in systematic bias. (3) Individualized equations using a minimum of 3 comparisons were required to reduce agreement to + or - 0.10 degrees C; the use of 5 comparisons minimized the number of readings exceeding + or - 0.10 degrees C. CONCLUSIONS: (1) The CorTemp system inflates temperature measurements compared with a mercury thermometer. (2) Individual sensor calibration is warranted. (3) Correction equations should use a minimum of 3, preferably 5, comparisons. After regression correction, the system displays satisfactory accuracy for preventative monitoring.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".