Functional Capacity Evaluation Research: Report from the Third International Functional Capacity Evaluation Research Meeting
Bibliographic record
Abstract
Purpose Based on the success of the first two conferences the Third International FCE Research Conference was held in The Netherlands on September 29, 2016. The aim was to provide ongoing opportunity to share and recent FCE research and discuss its implications. Methods Invitations and call for abstracts were sent to previous attendees, researchers, practicing FCE clinicians and professionals. Fifteen abstracts were selected for presentation. The FCE research conference contained two keynote lectures. Results 54 participants from 12 countries attended the conference where 15 research projects and 2 keynote lectures were presented. The conference provided an opportunity to present and discuss recent FCE research, and provided a forum for discourse related to FCE use. Conference presentations covered aspects of practical issues in administration and interpretation; protocol reliability and validity; consideration of specific injury populations; and a focused discussion on proposed inclusion of work physiology principles in FCE testing with the Heart Rate Reserve Method. Details of this Third International FCE Research Conference are available from http://repro.rcnheliomare.nl/FCE.pdf . Conclusions Researchers, clinicians, and other professionals in the FCE area have a common desire to further improve the content and quality of FCE research and to collaborate to further develop research across systems, cultures and countries. A fourth, 2-day, International FCE research conference will be held in Valens, Switzerland in August or September 2018. A 'FCE research Society' will be developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.147 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".