Use of health administrative databases in the analysis of waiting times and costs for the diagnosis of non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
6042 Background: Knowledge of waiting times and costs involved in diagnosis will help to define more effective diagnostic services. The use of administrative databases for such health services research provides a large, population-based cohort and permits an accurate calculation of costs. Methods: Patients diagnosed with NSCLC in Manitoba, Canada from 1996 to 2000 were identified by the cancer registry. Information on diagnostic and staging tests, timeline of investigations, physician visits, hospital admissions, and outcomes were obtained from detailed chart review for 472 patients and from the Manitoba Health Administrative Databases for 2,862 patients. Tariff codes for physician services were used to calculate costs. Results: An excellent correlation was observed (range 83.3 to 99.2%) between the chart review and the administrative databases with regards to timelines of imaging studies, physician services, and procedures. However, charts considerably underreported certain services. For instance, only 58% of chest x-rays performed were captured by chart review. Therefore, costs were analyzed using the administrative databases. Waiting times from chest x-ray to chest CT scan and from CT scan to definitive histologic diagnosis were a median of 8 (range 1–25) and 18 (range 3–42) days respectively by administrative databases, and 10 (range 1–31) and 18 (range 6–36) days respectively by chart review. At least 25% of 2862 patients waited more than 55 days from initial suspicion on chest x-ray to arrive at a final diagnosis. The mean cost to reach a lung cancer diagnosis was $7,578 but analysis indicates that there were large variations in costs among patients. The majority of expenses was accounted by hospital admissions and repeated primary care physician visits, while shorter waiting times to diagnosis corresponded with relatively less costs. Conclusions: Since there is an excellent correlation between chart review and administrative databases, the latter can be used in the future for large cohort studies instead of expensive, laborious chart reviews. Despite clinical suspicion, 25% of patients wait more than 8 weeks for a final diagnosis. A centralized diagnostic service may reduce waiting times and costs for diagnosis. No significant financial relationships to disclose.
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".