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RESPONSE: DEFINING EXERCISE CAPACITY, EXERCISE PERFORMANCE, AND A SEDENTARY LIFESTYLE

2002· article· en· W2015875367 on OpenAlexaff
Charli Sargent, G. C. Scroop

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2002
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsTerminologyPopulationMedicinePhysical therapySedentary lifestyleSuspectNormativePhysical medicine and rehabilitationPsychologyPhysical activity

Abstract

fetched live from OpenAlex

Dear Editor-in-Chief: The difficulty that White and Fulcher express in accepting our data may lie in a misinterpretation of the terminology and testing procedures used by exercise physiologists to define exercise status. Exercise capacity. The two critical measures of exercise capacity are maximal oxygen uptake and the lactate threshold (3,9). These measures can be precisely quantified according to well-accepted criteria (6) and were found to be normal in our study. Therefore, the claim that we “did not give the data” to support our finding is untrue. We suspect that they are confusing “exercise capacity” with “exercise performance” where the time taken to complete a given task or total work completed may be used as measures. Although no one denies that “exercise performance” is impaired in CFS, obtaining a useful index of performance in sedentary individuals is notoriously difficult (7,8) and almost impossible where there is a strong element of unexplained fatigue. Sedentary. “Sedentary” (2,11) is used to define normal, healthy individuals not engaged in regular, structured physical activity and normative population data abound to describe the exercise capacity of such individuals (1,5). The majority of individuals (> 70%) in Western societies can be categorized as “sedentary,” and their exercise capacity is appropriate to meet the demands of this lifestyle. Therefore, it is misleading to state, “patients with CFS were ...at least as deconditioned as sedentary healthy controls.” Gender. Comparisons of exercise status between subject groups that include members of both genders, whether the numbers in each group are the same or not, are meaningless. All normative population data (1,5), describing the exercise status of sedentary individuals, are routinely stratified on a gender basis. Exercise and CFS. Although it is well accepted that an appropriate graded exercise program will increase the exercise capacity of sedentary individuals, the central issue is whether CFS patients have a loss of normal, sedentary exercise capacity, which then requires correction. Our study (10) provided no evidence of a reduction in exercise capacity and, therefore, there is no physiological basis for instituting an exercise-training program. We feel that much of the confusion that White and Fulcher find between our study and those of other authors resides in the definitions used and the exercise tests employed. From our reading, much of the exercise testing in CFS patients has been directed at exercise performance. Hence, the widely reported reductions in maximal heart rate, ventilation, workload, and peak oxygen uptake are not unexpected. They almost certainly reflect the outcome of symptom-limited protocols where tests were terminated prematurely by unexplained fatigue rather than a reduced exercise capacity, an opinion shared by Fischler et al. (4). We found (10) that the “metabolic engine” of CFS patients was not different from that expected in healthy sedentary individuals of a similar age and gender. Therefore, the cause of their fatigue, which severely reduces exercise performance and results in extreme postexercise exhaustion, must be sought elsewhere. Charli Sargent, B App Sci, B Sc (Hons) Garry Scroop, MD, PhD

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.027
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0270.031
Insufficient payload (model declined to judge)0.0180.011

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.

Opus teacher head0.026
GPT teacher head0.284
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations7
Published2002
Admission routes1
Has abstractyes

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