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Record W2007195092 · doi:10.3138/physio.60.4.341

Investigation of the Frequency and Force of Chest Vibration Performed by Physiotherapists

2008· article· en· W2007195092 on OpenAlexvenueno aff
Shashsa Li, Y.R. Silva

Bibliographic record

VenuePhysiotherapy Canada · 2008
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsnot available
Fundersnot available
KeywordsSupine positionMedicineVibrationPhysical therapyWork (physics)Chest painPhysical medicine and rehabilitationCardiologySurgeryAcousticsPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the frequency and force of chest vibration as applied by 18 physiotherapists working in a teaching hospital. METHOD: CHEST VIBRATION WAS APPLIED TO A HEALTHY ADULT MALE LYING SUPINE ON A PLINTH WITH SEVEN MOUNTED SENSORS MEASURING FREQUENCY AND FORCE, DURING THREE TEST CONDITIONS: (1) directly on the chest, (2) on the chest through a layer of sheet, and (3) on the chest through a layer of towelling. The influence of gender and current practice area (physiotherapists working in cardiopulmonary areas [cardiopulmonary physiotherapists] and physiotherapists who presently did not work in the cardiopulmonary area, but had treated cardiopulmonary patients within the last year [general practice physiotherapists]) on the frequency and force of chest vibrations was examined. RESULTS: Physiotherapists demonstrated a mean frequency of 5.7, 5.3, and 5 Hz and a mean maximum force of 272.78, 273.47, and 271.13 N for conditions 1, 2, and 3 respectively. There were no significant differences in the frequency or forces generated by vibration between cardiopulmonary and general practice physiotherapists, between genders, or among the three test conditions. CONCLUSIONS: Vibration frequency was lower and force higher than previously recorded. Force may vary depending on the patient. The addition of a sheet or towel did not affect the force or frequency of vibration compared to vibration performed directly on the chest.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2008
Admission routes1
Has abstractyes

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