Impact of Time to Diagnosis and Treatment in Head and Neck Cancer: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: An increased interval between symptomatic disease and treatment may negatively influence oncologic and/or functional outcomes in head and neck cancer (HNC). This systematic review aims to provide insight into the effects of time to treatment intervals on oncologic and functional outcomes in oral cavity, pharyngeal, and laryngeal cancer. DATA SOURCES: PubMed, EMBASE, and Cochrane library were searched. REVIEW METHODS: All studies on delay or time to diagnosis or treatment in oral, pharyngeal, and laryngeal cancer were included. Quality assessment was performed with an adjusted version of the Newcastle-Ottawa scale. Outcomes of interest were tumor volume, stage, recurrence, survival, patient-reported outcome measures (PROMs), toxicity, and functionality after treatment. RESULTS: A total of 51 studies were included. Current literature on the influence of delay in HNC is inconsistent but indicates higher stage and worse survival with longer delay. The effects on PROMs, toxicity, and functional outcome after treatment have not been investigated. The inconsistencies in outcomes were most likely caused by factors such as heterogeneity in study design, differences in the definitions of delay, bias of results, and incomplete adjustment for confounding factors in the included studies. CONCLUSION: Irrespective of the level of evidence, the unfavorable effects of delay on oncologic, functional, and psychosocial outcomes are undisputed. Timely treatment while maintaining high-quality diagnostic procedures and decision making reflects good clinical practice in our opinion. This review will pose practical and logistic challenges that will have to be overcome.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".