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Record W3167906996 · doi:10.1097/phm.0000000000001797

Pilot and Feasibility Studies in Rehabilitation Research

2021· article· en· W3167906996 on OpenAlexaff
David Lawson, Katie Mellor, K H Kim, Lawrence Mbuagbaw

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsMcMaster University
FundersBarts Charity
KeywordsMedicineRehabilitationPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Pilot and feasibility studies are conducted early in the clinical research pathway to evaluate whether a future, definitive study can or should be done and, if so, how. Poor planning and reporting of pilot and feasibility studies can compromise subsequent research efforts. Inappropriate labeling of studies as pilots also compromises education. In this review, first, a systematic survey of the current state of pilot and feasibility studies in rehabilitation research was performed, and second, recommendations were made for improvements to their design and reporting. In a random sample of 100 studies, half (49.5%) were randomized trials. Thirty (30.0%) and three (3.0%) studies used “pilot” and “feasibility” in the study title, respectively. Only one third (34.0%) of studies provided a primary objective related to feasibility. Most studies (92.0%) stated an intent for hypothesis testing. Although many studies (70.0%) mentioned outcomes related to feasibility in the methods, a third (30.0%) reported additional outcomes in the results and discussion only or commented on feasibility anecdotally. The reporting of progression plans to a main study (21.0%) and progression criteria (4.0%) was infrequent. Based on these findings, it is recommended that researchers correctly label studies as a pilot or feasibility design based on accepted definitions, explicitly state feasibility objectives, outcomes, and criteria for determining success of feasibility, justify the sample size, and appropriately interpret and report the implications of feasibility findings for the main future study.

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)0.013
metaresearch head score (Gemma)0.090
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.304
GPT teacher head0.588
Teacher spread0.284 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

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