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Record W4236341600 · doi:10.3138/ptc.68.4.gee

The TIDieR Checklist Will Benefit the Physiotherapy Profession

2016· editorial· en· W4236341600 on OpenAlexaffvenue
Tiê Parma Yamato, Christopher G. Maher, Bruno Tirotti Saragiotto, Anne M. Moseley, Tammy Hoffmann, Mark R. Elkins, Dina Brooks

Bibliographic record

VenuePhysiotherapy Canada · 2016
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsCanadian Physiotherapy Association
Fundersnot available
KeywordsChecklistPhysical therapyMedicinePhysical medicine and rehabilitationComputer sciencePsychology

Abstract

fetched live from OpenAlex

[Extract] Evidence-based practice involves physiotherapists incorporating high-quality clinical research on treatment efficacy into their clinical decision making. However, if clinical interventions are not adequately reported in the literature, physiotherapists face an important barrier to using effective interventions with their patients. Previous studies have reported that incomplete description of interventions is a problem in reports of randomized controlled trials in many health areas. One of these studies examined 133 trials of non-pharmacological interventions. The experimental intervention was inadequately described in more than 60% of the trials, and descriptions of the control interventions were even worse. A recent study evaluated the completeness of descriptions of the physiotherapy interventions in a sample of 200 randomized controlled trials published in 2013. Overall, the interventions were poorly described. For the intervention groups, about one-quarter of the trials did not fulfil at least half of the criteria. Reporting for the control groups was even worse, with around three-quarters of trials not fulfilling at least half of the criteria. In other words, for the majority of the physiotherapy trials, clinicians and researchers would be unable to replicate the interventions that were tested. Describing a treatment may seem like a simple task, but physiotherapy interventions can be very complex. Some interventions are multi-modal, involving the use of manual techniques, consumable materials, equipment, education, training, and feedback. Some interventions are tailored to each patient's specific health state, including the patient's immediate response to the application of the treatment. When the intervention involves a course of treatments, the intensity or dose may be progressed over time. The descriptions of physiotherapy interventions in trial reports often do not capture all these components of the interventions or detail their complexity.

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)0.036
metaresearch head score (Gemma)0.182
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.964
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.182
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0140.006
Science and technology studies0.0080.006
Scholarly communication0.0230.010
Open science0.0080.006
Research integrity0.0570.042
Insufficient payload (model declined to judge)0.0420.032

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 head0.021
GPT teacher head0.448
Teacher spread0.427 · 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.

Study designNot applicable
DomainReporting
GenreEditorial

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
Published2016
Admission routes2
Has abstractno

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