Relationship of quadriceps torque to early post-lung transplant functional recovery after centre-based and tele-rehabilitation
Bibliographic record
Abstract
Introduction: Quadriceps torque (QT) is not routinely measured in the lung transplant (LTx) setting, but may inform post-LTx outcomes. Methods: A prospective cohort study assessed isometric QT (MedUpTM dynamometer) at the start of pre-habilitation and between 8-12 weeks post-LTx while rehabilitation was undertaken. Between-group differences were examined for participants who completed in-person pre- and post-LTx rehab 3x/week between September 2019 and March 2020 and participants who underwent tele-rehab between March 2020 and January 2021 when COVID-19 restrictions were implemented. Relationship between pre-LTx QT and post-LTx short physical performance (SPPB) and 6-minute walk distance (6MWD) were examined. Results: 35 people have been included to date (21 men, 61 [56-66] years, 26 in-person rehab, nine tele-rehab). The tele-rehab group waited longer for LTx (median 4 [IQR 3-6] weeks vs. 20 [14-27] weeks, p=0.0006) and subsequently had a longer pre-habilitation period. There were no between-group differences in QT, SPPB, gait speed, five times sit to stand (5STS) or 6MWD pre-LTx and no differences in the change post-LTx. Pre-LTx QT was associated with post-LTx gait speed (0.46, p=0.006) and 5STS time (-0.37, p=0.03) but not 6MWD or total SPPB score. Post-LTx QT did not change from pre-LTx values (-0.6 Nm [-22 to 8], p=0.56). Conclusions: Preliminary findings show no functional differences between in-person and tele-rehab. Pre-LTx QT was associated with post-LTx measures of lower extremity function. There was no change from pre- to post-LTx QT, however QT measured at the start of pre-habilitation may have changed during the wait for LTx.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".