Wait times in the diagnosis and treatment of head and neck cancer: comparison between wait times in 1995 and 2005--a prospective study.
Bibliographic record
Abstract
INTRODUCTION: There are limited studies documenting current wait times in the diagnosis and treatment of head and neck cancer. The objective of this study was to describe wait times along the health care pathway of head and neck cancer patients referred to an oncology centre in Ontario in 2005 and compare these times with those in a similar cohort of patients enrolled in 1995. METHODS: A cross-sectional study investigating wait times in newly diagnosed patients with squamous cell carcinoma of the head and neck was undertaken at the Princess Margaret Hospital, Toronto, Ontario. Data were collected prospectively in 2005 and 1995. Patient interviewing and health care provider corroboration were employed for a sample size of 45 patients in 2005 and 40 patients in 1995. RESULTS: Median wait times in 2005 were similar to those in 1995 for each time period, except for the time from consultation with the first tertiary specialist to treatment, which was statistically significantly shorter in 1995 (25 days) than in 2005 (37 days), p = .002. The median time from initial primary care practitioner seen to treatment (total professional delay) was not different from 1995 (126 days) to 2005 (115 days), p = .47. On univariate analysis, there were no statistically significant differences in wait times in each time period based on site (p = .226 and .321, respectively) and overall stage (p = .655 and .538, respectively). CONCLUSIONS: Wait times for head and neck cancer treatment from the time of initial consultation with an oncologist have increased from 1995 to 2005.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".