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Record W3170782983 · doi:10.3138/jmvfh-2020-0060

Pilot study: The effectiveness of physiotherapy-led screening for patients requiring an orthopedic intervention

2021· article· en· W3170782983 on OpenAlexaffvenueabout
Mallory Pike, Lucie Campagna-Wilson, Kim Sears, Robert J. Warren, DOUGLAS A. LeGAY, Daniel Trudel

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsDalhousie UniversityQueen's UniversityCanadian Armed Forces
Fundersnot available
KeywordsOrthopedic surgeryMedicineTriageIntervention (counseling)Physical therapyOrthopedic ProceduresMedical emergencySurgeryNursing

Abstract

fetched live from OpenAlex

LAY SUMMARY In Canada, patients can wait over a year to be seen by an orthopedic surgeon. To reduce wait times, physiotherapists have been employed in some practice areas to triage patients prior to being seen by an orthopedic surgeon. This study looked at different forms of triage by using physiotherapists to screen electronic medical records (EMR) to determine if patients needed orthopedic intervention or conservative management. To guide the physiotherapists, a screening tool was created. The study compared the recommendations of the physiotherapists with those of an orthopedic surgeon. The results showed that, most of the time, physiotherapists using the screening tool successfully identified whether a patient needed to see an orthopedic surgeon or could be treated with physiotherapy. This type of screening process may decrease wait times to see an orthopedic surgeon and improve access to physiotherapy or other treatments.

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)0.006
metaresearch head score (Gemma)0.030
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.095
GPT teacher head0.468
Teacher spread0.373 · 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
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

Citations5
Published2021
Admission routes3
Has abstractyes

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