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Chronotherapy in head and neck cancer (HNC): A systematic review.

2023· article· en· W4379341453 on OpenAlexaff
Abdel‐Azez Abu‐Samak, Mohammad Abusamak, Haider Al‐Waeli, Wenji Cai, Mohammad Al-Tamimi, Faleh Tamimi, Belinda Nicolau

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of TorontoDalhousie UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineChronotherapy (sleep phase)Radiation therapyRandomized controlled trialHead and neck cancerSystematic reviewInternal medicineCancerOncologyChemotherapyClinical trialAdverse effectCINAHLPsychological interventionMEDLINEMorning

Abstract

fetched live from OpenAlex

e18016 Background: Chronotherapy in cancer is optimizing the administration time of anti-cancer treatment according to circadian rhythm and cellular phase to improve the efficacy against tumor cells while decreasing side effects on normal cells. Several randomized controlled trials (RCT) evaluated chronotherapy of chemotherapy (chrono-chemotherapy: time-specific infusions) and/or radiotherapy (chrono-radiotherapy: morning radiotherapy) in various cancers and reported improved treatment efficacy and reduced toxicity. However, the effect of chronotherapy in HNC treatment is unclear as there are no prior systematic reviews reported. Therefore, this systematic review summarizes available clinical evidence on the effect of chrono-chemotherapy (CCT) and chrono-radiotherapy (CRT) on treatment response and adverse events in HNC adult patients. Methods: We conducted a systematic search using Medical Subject Headings (MeSH) in four online databases (OVID, Embase, CINAHL and Scopus), and 6078 articles identified were published in English between the databases' inception date and June 30, 2022. We included original peer-reviewed retrospective and prospective human studies investigating CCT and/or CRT versus conventional treatments in HNC patients. We excluded articles that contained no abstract, were unrelated to HNC, were pre-clinical and case reports, or did not include time-specific interventions in their methods. Results: 16 studies were finally included. Overall, studies were heterogenous in demographics, study design, intervention, and outcome measures, thus meta-analysis could not be performed. Nine studies (RCT = 3, Non-RCT = 2 & Retro = 4) investigated CRT. Squamous cell carcinoma (SCC) was mainly reported as the primary tumor (stage I-II). 7/9 studies reported a significant reduction in the incidence of oral mucositis (Grade ≥3) in the CRT group as opposed to conventional radiotherapy (RT) groups (p < 0.05). In contrast, treatment response was investigated in 4/9 studies and was insignificant (p > 0.05). Seven studies (RCT = 5 & Retro = 2) investigated CCT in patients diagnosed with SSC (stage II-IV). All included studies had concurrent RT. Different chemotherapeutic agents were used in combinations or as a single agent, namely Cisplatin, 5- Fu, Paclitaxel and Docetaxel. CCT groups in all studies achieved a significant reduction in Nausea & Vomiting (Grade ≥3), while a significant reduction in Leukopenia & Thrombocytopenia (Grade ≥3) was reported in 2 studies only (p < 0.05). Treatment response (OS and PFS) was not significantly different (p > 0.05) between both groups except 2 studies reported significantly higher ORR in chronotherapy groups (p < 0.05). Conclusions: CCT and CRT in HNC treatment in most studies provided evidence of toxicity reduction while treatment response was maintained. However, large multicentric randomized controlled studies with standardized protocols and optimized designs are still needed.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.194
GPT teacher head0.559
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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