An Extra Letter, Care Gets Better? Informing General Practitioners about Planned Surgery for Head and Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate how general practitioners (GPs) value an additional letter from the hospital. This so-called preadmission letter informs the GP about planned surgery for head and neck cancer in one of their patients. DESIGN: Prospective survey among GPs by means of a questionnaire attached to the preadmission letter. SETTING: Department of Otolaryngology and Head and Neck Surgery of a tertiary care centre in the Netherlands and 104 different GPs in primary care. PARTICIPANTS: All GPs of patients undergoing surgery for head and neck cancer received the preadmission letter during a 1-year study period. MAIN OUTCOME MEASURES: GPs' appreciation of the received preadmission letter, GPs' opinion on the content of the preadmission letter, and GPs' general opinion on information provided by our hospital. RESULTS: Of the 145 preadmission letters sent during the study year, 115 questionnaires were returned (response rate of 79%). All GPs positively appreciated receiving the preadmission letter and considered its content relevant. They valued the letter, with a mean mark of 8.3 on a 10-point scale. The majority of the GPs agreed that the preadmission letter allows them to provide better care. CONCLUSIONS: GPs highly appreciate an extra letter informing them about intended surgery for head and neck cancer in one of their patients. Despite the basic content of the preadmission letter (five items only), the majority of GPs consider the information sufficient. The results of this study have led to the implementation of the preadmission letter to GPs of head and neck cancer patients on a permanent basis in our institution.
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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 | 0.005 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".