Deployment of integrated care services for patients with long-term oxygen therapy (LTOT): Role of frailty
Bibliographic record
Abstract
Patients receiving LTOT are frequent users of healthcare services. Pilot studies have shown that Integrated Care Services (IC) reduce hospitalizations in these patients and have also identified that deployment of ICS requires an operational definition of frailty that allows risk stratification of patients. Objective: To characterize the risk profile of LTOT patients in an urban area of 540.000 inh in order to allow the design of a one-year follow-up RCT to assess deployment of IC tailored by patient's frailty. Methods: Observational study examining 751 patient's records. We planned three home visits for health examination survey, assessment of determinants of frailty, measurement of arterial blood gases and perceived needs. Up to 423 (56%) patients with active LTOT were studied. Preliminary data from an unbiased sample of 282 patients are reported. Results: Eighty six patients (31%) had a P0 2 ≤ 55 mmHg and only 62 of them (22%) used LTOT ≥ 16 h/day. Most patients had never received an educational program (94%) or home care support (74%). Among several frailty indicators, the Canadian scale (CS) showed an association with the adequacy and hours of administration of LTOT. Patients with career but without other dependent persons at home [OR 3.56 (1.29–9.78)], higher CS of frailty [2.88 (1.09–7.60)] and high treatment score (5 to 9 drugs) [3.78 (1.05–13.6)] showed better LTOT adherence.Factors related with organization of healthcare services had impact on LTOT adherence [0.45 (0.22–0.91)]. Conclusions: The study provides the rationale for future actions on modifiable factors aiming at enhancing quality of LTOT. Supported by Nexes (FP7-CIP-ICT- 225025) and Esteve-Teijin.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".