Pre‐transplant short physical performance battery: Response to pre‐habilitation and relationship to pre‐ and early post–lung‐transplant outcomes
Bibliographic record
Abstract
PURPOSE: To evaluate whether the short physical performance battery (SPPB) pre-lung transplant (LTx) was responsive to pre-habilitation and predicted pre- and early post-transplant outcomes. METHODS: A retrospective study of LTx candidates accepted for transplant between 2016 and 2017. SPPB was categorized as frail/pre-frail (≤9/12) and non-frail (≥10/12). RESULTS: 150 patients had LTx assessment SPPB data (53% male, 61 [52-67] years, 59% had interstitial lung disease (ILD), 26% frail/pre-frail). 131 (87%) underwent transplant by December 31, 2018. Adjusting for age, sex, diagnosis and Canadian transplant listing urgency, and frailty/pre-frailty at LTx assessment was associated with a lower 6MWD pre-transplant [-89 meters 95%CI (-125 to -53), P < .0001]. 62 patients underwent six weeks of pre-habilitation. SPPB increased (11 [10-12) vs. 12 [11-12], P = .01) reflected in the chair stand component (11.4 ± 4.4 vs. 9.8 ± 2.8 seconds, P = .007), with larger improvements in the frail/pre-frail group. A frail/pre-frail SPPB closest to the time of transplant was associated with a lower 6MWD [-77 m 95%CI (-128 to -25), P = .004] but not with hospital length of stay or gait aid use three months post-transplant. CONCLUSIONS: Frailty/pre-frailty was associated with a decreased 6MWD pre- and post-transplant. The SPPB increased following pre-habilitation, which may reflect increased lower extremity strength.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".