Influence of mode of training and gender on Borg's Rating of Perceived Exertion in cardiac rehabilitation / by Chris Carruthers. --
Bibliographic record
Abstract
The influence of gender and training mode (treadmill, \ncycle, swimming and volleyball) on Borg's Rating of Perceived \nExertion (RPE) and heart rate was investigated in a Phase III \ncardiac rehabilitation program. Twenty-three patients with \nischemic heart disease (IHD) participated for eight months in \na triweekly 60 minute exercise session where RPE and heart \nrate were monitored at peak activity. The results indicated \nthat both male and female IHD paticents have equal ability to \nperceive effort, A significant difference (p>.05) was found \nin the influence of training mode on both RPE and heart rate \nin the case of volleyball, although the subjects were \nrequired to train on all four modes within a narrow heart \nrate range. An analysis of individual trends showed that \ncertain subjects were able to rate RPE consistently with \nheart rate over all four modes, while others were not. \nOverall the subjects were consistent at rating RPE within a \nnarrow heart rate range (12 beats per minuter) 667, of the \ntime. These findings reveal that RPE is a valid general \nindicator of work intensity on various modes. It is \nrecommended that the RPE scale be used with caution for IHD \npatients requiring strict, monitoring of exercise intensity, \nand that it be used for intraindividual comparisons only.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".