Authors' Response
Bibliographic record
Abstract
Authors' Response: We thank Reabias de Andrade Pereira and Dr. Alexandra Sergio Silva for their interest in our article “Validity of the Elite HRV Smart Phone Application for Examining Heart Rate Variability in a Field Based Setting” (4) and the time they provided to expand on our discoveries. We acknowledge the form of validity the manuscript clarification authors are referring to as “criterion validity” and agree this type of validity was not examined in our article. However, this was not the intent of our investigation. The criterion validity the manuscript clarification authors are referring to is an assessment of the sensitivity/sampling frequency between an electrocardiograph and a heart rate monitor to collect cardiac cycles, not the direct examination of the HRV software to collect, analyze, correct for artifact, and produce a valid root mean square of the successive difference value as it is intended to. The purpose of our investigation was to assess the validity of a smartphone application (i.e., software) that when compared against the accepted gold standard, Kubios HRV 2.2 (Biosignal Analysis and Medical Imaging Group at the Department of Applied Physics, University of Kuopio, Kuopio, Finland), when using an ECG-validated heart rate monitor (3), in a field-based setting, would elicit root mean square of the successive difference values within the acceptable levels of agreement (2). We must acknowledge the distinct types of measurement validity for quantitative and qualitative research beyond that of criterion validity, such as content validity and construct validity (1). This is especially concerning for sport practitioners who are considering the introduction or interchanging of different HRV software after standardizing their collection of cardiac cycles through an ECG-validated heart rate monitor. We have encouraged further investigations to support or disprove our findings using similar or different approaches before a definitive statement toward its validation is provided. We thank the manuscript clarification authors again for their interest in our article and the time they provided to help expand on its discoveries.
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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 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.127 | 0.073 |
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".