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The 220‐age equation does not predict maximum heart rate in children and adolescents

2011· letter· en· W1991173234 on OpenAlexaff
Olaf Verschuren, Désirée B. Maltais, Tim Takken

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2011
Typeletter
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsAmbulatoryGross Motor Function Classification SystemCerebral palsyHeart rateSpasticMedicineConfidence intervalPhysical therapyPediatricsInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Our primary purpose was to provide maximum heart rate (HR(max) ) values for ambulatory children with cerebral palsy (CP). The secondary purpose was to determine the effects of age, sex, ambulatory ability, height, and weight on HR(max) . In 362 ambulatory children and adolescents with CP (213 males and 149 females; age range 6-19y; 195 spastic unilateral, 162 spastic bilateral, and five ataxic CP), HR(max) was measured during a 10-m (Gross Motor Function Classification System [GMFCS] levels I and II) or 7.5 m (GMFCS level III) shuttle run test. The mean HR(max) was 194 (SD 9.9) beats per minute (bpm), with a 95% prediction interval between 174 and 214 bpm. No associations were found in HR(max) related to age, sex, ambulatory ability, height, and weight. Since the HR(max) did not vary with age, equations such as 220-age are not appropriate. When direct evaluation of HR(max) with exercise testing is not feasible, we suggest the mean value of 194 bpm be considered as an estimate of HR(max) at the individual level.

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)0.001
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 designObservational
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

Citations74
Published2011
Admission routes1
Has abstractyes

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