Preliminary Development and Validation of a Paediatric Cardiopulmonary Physiotherapy Discharge Tool
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop a paediatric cardiopulmonary physiotherapy (CPT) discharge tool. We report on the initial stages of its development and the tool's sensibility (face/content validity, feasibility, and ease of usage). METHODS: Using a modified Delphi technique, a panel of paediatric physiotherapy clinicians and academic leaders in the area of CPT (n=25) was recruited. Four rounds of discussion among the members of the Delphi panel focused on (1) generation of discharge items, (2) reduction of items, (3) discussion of contentious items and refinement of criterion definitions, and (4) determination of scoring options for the test instrument. The sensibility of a draft of the tool was assessed using a sample of convenience (n=15). RESULTS: Six items (auscultation, discharge planning, mobility, oxygen saturation, secretion clearance, and signs of respiratory distress) were identified for inclusion in the tool. The global mean of all sensibility domains was 6.4 (median=6.6) of a possible 7.0. CONCLUSION: Using a modified Delphi process, we developed a six-item paediatric CPT discharge planning tool with good face and content validity. Future work will determine the scoring method for using this tool, interrater reliability, and predictive validity to facilitate optimal timing of hospital discharge for paediatric CPT patients.
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 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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".