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Record W2119686981 · doi:10.3138/ptc.59.2.86

Cervical Manipulation and Informed Consent: Canadian Manipulative Physiotherapists' Opinions on Communicating Risk

2007· article· en· W2119686981 on OpenAlexvenueaboutno aff
Lisa C. Carlesso, Doreen J. Bartlett, Beverley Padfield, Bert M. Chesworth

Bibliographic record

VenuePhysiotherapy Canada · 2007
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsInformed consentMedicineConfidence intervalFact sheetDelphi methodGuidelineFamily medicineDelphiSample (material)Physical therapyAlternative medicineComputer science

Abstract

fetched live from OpenAlex

Purpose: The study objective was to generate an information sheet for Canadian manipulative physical therapists (CMPTs) to use when seeking informed consent for high-velocity, low-amplitude cervical manipulation. Methods: A cervical manipulation information sheet (CMIS) was created with five sections: Introduction, Benefits, Risks, Procedures and Effectiveness. The content of the information sheet was generated using the Delphi method, followed by a mail-out survey to a random sample of CMPTs (N = 307) to determine the information sheet's acceptability and clinical utility. The proportion of CMPTs who agreed with the content of the information sheet and the proportion of CMPTs who indicated a willingness to use the sheet clinically were calculated. Results: The survey response rate was 74 per cent. The proportion (95 per cent confidence interval) of respondents who agreed with the content of the CMIS and approved its clinical acceptability was 0.95 (0.94-0.96) and 0.61 (0.58-0.64), respectively. Written comments from the CMPTs reflected concern about wording in the Risks section. Conclusions: Our results suggest that CMPTs agreed with the content of the CMIS but not how it was written. Physical therapists should consider the CMIS a proposed guideline for clinician use. Ideally, a patient version should also be created.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.158
GPT teacher head0.461
Teacher spread0.303 · 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 designObservational
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

Citations8
Published2007
Admission routes2
Has abstractyes

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