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Record W2033540753 · doi:10.3138/ptc.2013-42

Developing a Physiotherapy-Specific Preliminary Clinical Decision-Making Tool for Oxygen Titration: A Modified Delphi Study

2014· article· en· W2033540753 on OpenAlexaffvenueabout
Michelle Duong, Kendra Bertin, Renee Henry, Deepti Singh, Nolla Timmins, Dina Brooks, Sunita Mathur, Cindy Ellerton

Bibliographic record

VenuePhysiotherapy Canada · 2014
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLikert scaleDelphi methodContext (archaeology)UsabilityDelphiPhysical therapyMedicinePsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: To develop and evaluate a preliminary clinical decision-making tool (CDMT) to assist physiotherapists in titrating oxygen for acutely ill adults in Ontario. METHODS: A panel of 14 experienced cardiorespiratory physiotherapists was recruited. Factors relating to oxygen titration were identified using a modified Delphi technique. Four rounds of questionnaires were conducted, during which the goals were to (1) generate factors, (2) reduce factors and debate contentious factors, (3) finalize factors and develop the preliminary CDMT, and (4) evaluate the usability of the tool in a clinical context. RESULTS: The panel reached consensus on a total of 89 factors, which were compiled to create the preliminary CDMT. The global tool reached consensus for sensibility, receiving a mean score of 6/7 on a 7-point Likert-type scale (1=unacceptable; 7=excellent). Five of the nine individual components of evaluation of the tool achieved scores ≥6.0; the remaining four had mean scores between 5.4 and 5.9. CONCLUSION: This study produced a preliminary CDMT for oxygen titration, which the panel agreed was highly comprehensible and globally sensible. Further research is necessary to evaluate the sensibility and applicability of the tool in a clinical setting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.043
GPT teacher head0.377
Teacher spread0.334 · 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.

Study designOther design
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

Citations4
Published2014
Admission routes3
Has abstractyes

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